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2005


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Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Tsuda, K., Rätsch, G., Warmuth, M.

Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

Abstract
We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the exponentiated gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the derivation and the analyses of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the resulting matrix exponentiated gradient (MEG) update and DefiniteBoost to the problem of learning a kernel matrix from distance measurements.

ei

PDF [BibTex]

2005


PDF [BibTex]


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Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Journal of the Optical Society of America A, 22(5):801-809, May 2005 (article)

Abstract
A number of models of depth cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum variance unbiased estimator that can be constructed from the available information. Here we test such models using visual and haptic depth information. Different texture types produce differences in slant discrimination performance, providing a means for testing a reliability-sensitive cue combination model using texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability, but fell short of statistically optimal combination—we find reliability-based re-weighting, but not statistically optimal cue combination.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian inference for psychometric functions

Kuss, M., Jäkel, F., Wichmann, F.

Journal of Vision, 5(5):478-492, May 2005 (article)

Abstract
In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer’s ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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A gene expression map of Arabidopsis thaliana development

Schmid, M., Davison, T., Henz, S., Pape, U., Demar, M., Vingron, M., Schölkopf, B., Weigel, D., Lohmann, J.

Nature Genetics, 37(5):501-506, April 2005 (article)

Abstract
Regulatory regions of plant genes tend to be more compact than those of animal genes, but the complement of transcription factors encoded in plant genomes is as large or larger than that found in those of animals. Plants therefore provide an opportunity to study how transcriptional programs control multicellular development. We analyzed global gene expression during development of the reference plant Arabidopsis thaliana in samples covering many stages, from embryogenesis to senescence, and diverse organs. Here, we provide a first analysis of this data set, which is part of the AtGenExpress expression atlas. We observed that the expression levels of transcription factor genes and signal transduction components are similar to those of metabolic genes. Examining the expression patterns of large gene families, we found that they are often more similar than would be expected by chance, indicating that many gene families have been co-opted for specific developmental processes.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Experimentally optimal v in support vector regression for different noise models and parameter settings

Chalimourda, A., Schölkopf, B., Smola, A.

Neural Networks, 18(2):205-205, March 2005 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Adhesive microstructure and method of forming same

Fearing, R. S., Sitti, M.

March 2005, US Patent 6,872,439 (misc)

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[BibTex]

[BibTex]


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Active Learning for Parzen Window Classifier

Chapelle, O.

In AISTATS 2005, pages: 49-56, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

Abstract
The problem of active learning is approached in this paper by minimizing directly an estimate of the expected test error. The main difficulty in this ``optimal'' strategy is that output probabilities need to be estimated accurately. We suggest here different methods for estimating those efficiently. In this context, the Parzen window classifier is considered because it is both simple and probabilistic. The analysis of experimental results highlights that regularization is a key ingredient for this strategy.

ei

Web [BibTex]

Web [BibTex]


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Semi-Supervised Classification by Low Density Separation

Chapelle, O., Zien, A.

In AISTATS 2005, pages: 57-64, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

Abstract
We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing the Transductive SVM objective function, which places the decision boundary in low density regions, by gradient descent; 3. combining the first two to make maximum use of the cluster assumption. We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Constrained Covariance for Dependence Measurement

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Pauls, J., Schölkopf, B., Logothetis, N.

In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pages: 112-119, (Editors: R Cowell, R and Z Ghahramani), AISTATS, January 2005 (inproceedings)

Abstract
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables in all circumstances. Dependent random variables can result in a COCO which is arbitrarily close to zero when the source densities are highly non-smooth. All current kernel-based independence tests share this behaviour. We demonstrate exponential convergence between the population and empirical COCO. Finally, we use COCO as a measure of joint neural activity between voxels in MRI recordings of the macaque monkey, and compare the results to the mutual information and the correlation. We also show the effect of removing breathing artefacts from the MRI recording.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

In AISTATS 2005, pages: 136-143, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

Abstract
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and two image classification problems.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Composite adaptive control with locally weighted statistical learning

Nakanishi, J., Farrell, J. A., Schaal, S.

Neural Networks, 18(1):71-90, January 2005, clmc (article)

Abstract
This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters. We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.

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link (url) [BibTex]

link (url) [BibTex]


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Invariance of Neighborhood Relation under Input Space to Feature Space Mapping

Shin, H., Cho, S.

Pattern Recognition Letters, 26(6):707-718, 2005 (article)

Abstract
If the training pattern set is large, it takes a large memory and a long time to train support vector machine (SVM). Recently, we proposed neighborhood property based pattern selection algorithm (NPPS) which selects only the patterns that are likely to be near the decision boundary ahead of SVM training [Proc. of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Lecture Notes in Artificial Intelligence (LNAI 2637), Seoul, Korea, pp. 376–387]. NPPS tries to identify those patterns that are likely to become support vectors in feature space. Preliminary reports show its effectiveness: SVM training time was reduced by two orders of magnitude with almost no loss in accuracy for various datasets. It has to be noted, however, that decision boundary of SVM and support vectors are all defined in feature space while NPPS described above operates in input space. If neighborhood relation in input space is not preserved in feature space, NPPS may not always be effective. In this paper, we sh ow that the neighborhood relation is invariant under input to feature space mapping. The result assures that the patterns selected by NPPS in input space are likely to be located near decision boundary in feature space.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Intrinsic Dimensionality Estimation of Submanifolds in Euclidean space

Hein, M., Audibert, Y.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 289 , (Editors: De Raedt, L. , S. Wrobel), ICML Bonn, 2005 (inproceedings)

Abstract
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean space from random samples. The method is based on the convergence rates of a certain U-statistic on the manifold. We solve at least partially the question of the choice of the scale of the data. Moreover the proposed method is easy to implement, can handle large data sets and performs very well even for small sample sizes. We compare the proposed method to two standard estimators on several artificial as well as real data sets.

ei

PDF [BibTex]

PDF [BibTex]


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Large Scale Genomic Sequence SVM Classifiers

Sonnenburg, S., Rätsch, G., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 849-856, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve sufficiently high classification performances. In this work we study two recently proposed and successfully used kernels, namely the Spectrum kernel and the Weighted Degree kernel (WD). In particular, we suggest several extensions using Suffix Trees and modi cations of an SMO-like SVM training algorithm in order to accelerate the training of the SVMs and their evaluation on test sequences. Our simulations show that for the spectrum kernel and WD kernel, large scale SVM training can be accelerated by factors of 20 and 4 times, respectively, while using much less memory (e.g. no kernel caching). The evaluation on new sequences is often several thousand times faster using the new techniques (depending on the number of Support Vectors). Our method allows us to train on sets as large as one million sequences.

ei

PDF [BibTex]

PDF [BibTex]


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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O.

In Proceedings of the 8th InternationalWork-Conference on Artificial Neural Networks, LNCS 3512, pages: 176-191, (Editors: J Cabestany and A Prieto and F Sandoval), Springer, Berlin Heidelberg, Germany, IWANN, 2005 (inproceedings)

Abstract
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension, e.g., thousands of dimensions. This is achieved by mapping the objects into continuous or discrete spaces, using joint kernels. Known correlations between input and output can be defined by such kernels, some of which can maintain linearity in the outputs to provide simple (closed form) pre-images. We provide examples of such kernels and empirical results.

ei

PostScript DOI [BibTex]

PostScript DOI [BibTex]


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Analysis of Some Methods for Reduced Rank Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

In Switching and Learning in Feedback Systems, pages: 98-127, (Editors: Murray Smith, R. , R. Shorten), Springer, Berlin, Germany, European Summer School on Multi-Agent Control, 2005 (inproceedings)

Abstract
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning the covariance function hyperparameters and the support set. We propose a method for learning hyperparameters for a given support set. We also review the Sparse Greedy GP (SGGP) approximation (Smola and Bartlett, 2001), which is a way of learning the support set for given hyperparameters based on approximating the posterior. We propose an alternative method to the SGGP that has better generalization capabilities. Finally we make experiments to compare the different ways of training a RRGP. We provide some Matlab code for learning RRGPs.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Theory of Classification: A Survey of Some Recent Advances

Boucheron, S., Bousquet, O., Lugosi, G.

ESAIM: Probability and Statistics, 9, pages: 323 , 2005 (article)

Abstract
The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have lead to these important recent developments.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians

Hein, M., Audibert, J., von Luxburg, U.

In Proceedings of the 18th Conference on Learning Theory (COLT), pages: 470-485, Conference on Learning Theory, 2005, Student Paper Award (inproceedings)

Abstract
In the machine learning community it is generally believed that graph Laplacians corresponding to a finite sample of data points converge to a continuous Laplace operator if the sample size increases. Even though this assertion serves as a justification for many Laplacian-based algorithms, so far only some aspects of this claim have been rigorously proved. In this paper we close this gap by establishing the strong pointwise consistency of a family of graph Laplacians with data-dependent weights to some weighted Laplace operator. Our investigation also includes the important case where the data lies on a submanifold of $R^d$.

ei

PDF [BibTex]

PDF [BibTex]


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Propagating Distributions on a Hypergraph by Dual Information Regularization

Tsuda, K.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 921 , (Editors: De Raedt, L. , S. Wrobel), ICML Bonn, 2005 (inproceedings)

Abstract
In the information regularization framework by Corduneanu and Jaakkola (2005), the distributions of labels are propagated on a hypergraph for semi-supervised learning. The learning is efficiently done by a Blahut-Arimoto-like two step algorithm, but, unfortunately, one of the steps cannot be solved in a closed form. In this paper, we propose a dual version of information regularization, which is considered as more natural in terms of information geometry. Our learning algorithm has two steps, each of which can be solved in a closed form. Also it can be naturally applied to exponential family distributions such as Gaussians. In experiments, our algorithm is applied to protein classification based on a metabolic network and known functional categories.

ei

[BibTex]

[BibTex]


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Support Vector Machines and Kernel Algorithms

Schölkopf, B., Smola, A.

In Encyclopedia of Biostatistics (2nd edition), Vol. 8, 8, pages: 5328-5335, (Editors: P Armitage and T Colton), John Wiley & Sons, NY USA, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Moment Inequalities for Functions of Independent Random Variables

Boucheron, S., Bousquet, O., Lugosi, G., Massart, P.

To appear in Annals of Probability, 33, pages: 514-560, 2005 (article)

Abstract
A general method for obtaining moment inequalities for functions of independent random variables is presented. It is a generalization of the entropy method which has been used to derive concentration inequalities for such functions cite{BoLuMa01}, and is based on a generalized tensorization inequality due to Lata{l}a and Oleszkiewicz cite{LaOl00}. The new inequalities prove to be a versatile tool in a wide range of applications. We illustrate the power of the method by showing how it can be used to effortlessly re-derive classical inequalities including Rosenthal and Kahane-Khinchine-type inequalities for sums of independent random variables, moment inequalities for suprema of empirical processes, and moment inequalities for Rademacher chaos and $U$-statistics. Some of these corollaries are apparently new. In particular, we generalize Talagrands exponential inequality for Rademacher chaos of order two to any order. We also discuss applications for other complex functions of independent random variables, such as suprema of boolean polynomials which include, as special cases, subgraph counting problems in random graphs.

ei

PDF [BibTex]

PDF [BibTex]


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A Brain Computer Interface with Online Feedback based on Magnetoencephalography

Lal, T., Schröder, M., Hill, J., Preissl, H., Hinterberger, T., Mellinger, J., Bogdan, M., Rosenstiel, W., Hofmann, T., Birbaumer, N., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 465-472, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto- noise ratio, is likely to succeed. We apply recursive channel elimination and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier together with a decision tree interface to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online BCI based on MEG recordings and is therefore a “proof of concept”.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Healing the Relevance Vector Machine through Augmentation

Rasmussen, CE., Candela, JQ.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 689 , (Editors: De Raedt, L. , S. Wrobel), ICML, 2005 (inproceedings)

Abstract
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties have the unintuitive property, that emph{they get smaller the further you move away from the training cases}. We give a thorough analysis. Inspired by the analogy to non-degenerate Gaussian Processes, we suggest augmentation to solve the problem. The purpose of the resulting model, RVM*, is primarily to corroborate the theoretical and experimental analysis. Although RVM* could be used in practical applications, it is no longer a truly sparse model. Experiments show that sparsity comes at the expense of worse predictive distributions.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Visual perception I: Basic principles

Wagemans, J., Wichmann, F., de Beeck, H.

In Handbook of Cognition, pages: 3-47, (Editors: Lamberts, K. , R. Goldstone), Sage, London, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach

Jung, T., Herrera, L., Schölkopf, B.

In Proceedings of the 8th International Work-Conferenceon Artificial Neural Networks (Computational Intelligence and Bioinspired Systems), Lecture Notes in Computer Science, Vol. 3512, LNCS 3512, pages: 960-967, (Editors: J Cabestany and A Prieto and F Sandoval), Springer, Berlin Heidelberg, Germany, IWANN, 2005 (inproceedings)

Abstract
In this paper we report the results obtained using a kernel-based approach to predict the temporal development of four response signals in the process control of a glass melting tank with 16 input parameters. The data set is a revised version1 from the modelling challenge in EUNITE-2003. The central difficulties are: large time-delays between changes in the inputs and the outputs, large number of data, and a general lack of knowledge about the relevant variables that intervene in the process. The methodology proposed here comprises Support Vector Machines (SVM) and Regularization Networks (RN). We use the idea of sparse approximation both as a means of regularization and as a means of reducing the computational complexity. Furthermore, we will use an incremental approach to add new training examples to the kernel-based method and efficiently update the current solution. This allows us to use a sophisticated learning scheme, where we iterate between prediction and training, with good computational efficiency and satisfactory results.

ei

DOI [BibTex]

DOI [BibTex]


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Object correspondence as a machine learning problem

Schölkopf, B., Steinke, F., Blanz, V.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 777-784, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points of one object will be mapped to ``similar‘‘ points on the other one. Our system, which contains little engineering or domain knowledge, delivers state of the art performance. We present application results including close to photorealistic morphs of 3D head models.

ei

PDF [BibTex]

PDF [BibTex]


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A tutorial on v-support vector machines

Chen, P., Lin, C., Schölkopf, B.

Applied Stochastic Models in Business and Industry, 21(2):111-136, 2005 (article)

Abstract
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.

ei

PDF [BibTex]

PDF [BibTex]


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Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces

Schröder, M., Lal, T., Hinterberger, T., Bogdan, M., Hill, J., Birbaumer, N., Rosenstiel, W., Schölkopf, B.

EURASIP Journal on Applied Signal Processing, 2005(19, Special Issue: Trends in Brain Computer Interfaces):3103-3112, (Editors: Vesin, J. M., T. Ebrahimi), 2005 (article)

Abstract
Most EEG-based Brain Computer Interface (BCI) paradigms come along with specific electrode positions, e.g.~for a visual based BCI electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects Lal et.~al showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extend their method of Recursive Channel Elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Implicit Surface Modelling as an Eigenvalue Problem

Walder, C., Chapelle, O., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 937-944, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
We discuss the problem of fitting an implicit shape model to a set of points sampled from a co-dimension one manifold of arbitrary topology. The method solves a non-convex optimisation problem in the embedding function that defines the implicit by way of its zero level set. By assuming that the solution is a mixture of radial basis functions of varying widths we attain the globally optimal solution by way of an equivalent eigenvalue problem, without using or constructing as an intermediate step the normal vectors of the manifold at each data point. We demonstrate the system on two and three dimensional data, with examples of missing data interpolation and set operations on the resultant shapes.

ei

PDF [BibTex]

PDF [BibTex]


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Nonlinear optical spectroscopy of chiral molecules

Fischer, P., Hache, F.

CHIRALITY, 17(8):421-437, 2005 (article)

Abstract
We review nonlinear optical processes that are specific to chiral molecules in solution and on surfaces. In contrast to conventional natural optical activity phenomena, which depend linearly on the electric field strength of the optical field, we discuss how optical processes that are nonlinear (quadratic, cubic, and quartic) functions of the electromagnetic field strength may probe optically active centers and chiral vibrations. We show that nonlinear techniques open entirely new ways of exploring chirality in chemical and biological systems: The cubic processes give rise to nonlinear circular dichroism and nonlinear optical rotation and make it possible to observe dynamic chiral processes at ultrafast time scales. The quadratic second-harmonic and sum-frequency-generation phenomena and the quartic processes may arise entirely in the electric-dipole approximation and do not require the use of circularly polarized light to detect chirality: They provide surface selectivity and their observables can be relatively much larger than in linear optical activity. These processes also give rise to the generation of light at a new color, and in liquids this frequency conversion only occurs if the solution is optically active. We survey recent chiral nonlinear optical experiments and give examples of their application to problems of biophysical interest. (C) 2005 Wiley-Liss, Inc.

pf

DOI [BibTex]

DOI [BibTex]


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Negative refraction at optical frequencies in nonmagnetic two-component molecular media

Chen, Y., Fischer, P., Wise, F.

PHYSICAL REVIEW LETTERS, 95(6), 2005 (article)

Abstract
There is significant motivation to develop media with negative refractive indices at optical frequencies, but efforts in this direction are hampered by the weakness of the magnetic response at such frequencies. We show theoretically that a nonmagnetic medium with two atomic or molecular constituents can exhibit a negative refractive index. A negative index is possible even when the real parts of both the permittivity and permeability are positive. This surprising result provides a route to isotropic negative-index media at optical frequencies.

pf

DOI [BibTex]

DOI [BibTex]


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Natural Actor-Critic

Peters, J., Vijayakumar, S., Schaal, S.

In Proceedings of the 16th European Conference on Machine Learning, 3720, pages: 280-291, (Editors: Gama, J.;Camacho, R.;Brazdil, P.;Jorge, A.;Torgo, L.), Springer, ECML, 2005, clmc (inproceedings)

Abstract
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing AmariÕs natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regres- sion. We show that actor improvements with natural policy gradients are particularly appealing as these are independent of coordinate frame of the chosen policy representation, and can be estimated more efficiently than regular policy gradients. The critic makes use of a special basis function parameterization motivated by the policy-gradient compatible function approximation. We show that several well-known reinforcement learning methods such as the original Actor-Critic and BradtkeÕs Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms. Em- pirical evaluations illustrate the effectiveness of our techniques in com- parison to previous methods, and also demonstrate their applicability for learning control on an anthropomorphic robot arm.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Comparative experiments on task space control with redundancy resolution

Nakanishi, J., Cory, R., Mistry, M., Peters, J., Schaal, S.

In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3901-3908, Edmonton, Alberta, Canada, Aug. 2-6, IROS, 2005, clmc (inproceedings)

Abstract
Understanding the principles of motor coordination with redundant degrees of freedom still remains a challenging problem, particularly for new research in highly redundant robots like humanoids. Even after more than a decade of research, task space control with redundacy resolution still remains an incompletely understood theoretical topic, and also lacks a larger body of thorough experimental investigation on complex robotic systems. This paper presents our first steps towards the development of a working redundancy resolution algorithm which is robust against modeling errors and unforeseen disturbances arising from contact forces. To gain a better understanding of the pros and cons of different approaches to redundancy resolution, we focus on a comparative empirical evaluation. First, we review several redundancy resolution schemes at the velocity, acceleration and torque levels presented in the literature in a common notational framework and also introduce some new variants of these previous approaches. Second, we present experimental comparisons of these approaches on a seven-degree-of-freedom anthropomorphic robot arm. Surprisingly, one of our simplest algorithms empirically demonstrates the best performance, despite, from a theoretical point, the algorithm does not share the same beauty as some of the other methods. Finally, we discuss practical properties of these control algorithms, particularly in light of inevitable modeling errors of the robot dynamics.

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A piezoelectric unimorph actuator based precision positioning miniature walking robot

Son, K. J., Kartik, V., Wickert, J. A., Sitti, M.

In Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, pages: 176-182, 2005 (inproceedings)

[BibTex]

[BibTex]


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Modeling and testing of a biomimetic flagellar propulsion method for microscale biomedical swimming robots

Behkam, B., Sitti, M.

In Proceedings of Advanced Intelligent Mechatronics Conference, pages: 37-42, 2005 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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A model of smooth pursuit based on learning of the target dynamics using only retinal signals

Shibata, T., Tabata, H., Schaal, S., Kawato, M.

Neural Networks, 18, pages: 213-225, 2005, clmc (article)

Abstract
While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

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link (url) [BibTex]

link (url) [BibTex]


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Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares

Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., Schaal, S.

In Advances in Neural Information Processing Systems 18 (NIPS 2005), (Editors: Weiss, Y.;Schölkopf, B.;Platt, J.), Cambridge, MA: MIT Press, Vancouver, BC, Dec. 6-11, 2005, clmc (inproceedings)

Abstract
An increasing number of projects in neuroscience requires the statistical analysis of high dimensional data sets, as, for instance, in predicting behavior from neural firing, or in operating artificial devices from brain recordings in brain-machine interfaces. Linear analysis techniques remain prevalent in such cases, but classi-cal linear regression approaches are often numercially too fragile in high dimen-sions. In this paper, we address the question of whether EMG data collected from arm movements of monkeys can be faithfully reconstructed with linear ap-proaches from neural activity in primary motor cortex (M1). To achieve robust data analysis, we develop a full Bayesian approach to linear regression that automatically detects and excludes irrelevant features in the data, and regular-izes against overfitting. In comparison with ordinary least squares, stepwise re-gression, partial least squares, and a brute force combinatorial search for the most predictive input features in the data, we demonstrate that the new Bayesian method offers a superior mixture of characteristics in terms of regularization against overfitting, computational efficiency, and ease of use, demonstrating its potential as a drop-in replacement for other linear regression techniques. As neuroscientific results, our analyses demonstrate that EMG data can be well pre-dicted from M1 neurons, further opening the path for possible real-time inter-faces between brains and machines.

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link (url) [BibTex]

link (url) [BibTex]


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From colloidal Co/CoO core/shell nanoparticles to arrays of metallic nanomagnets: surface modification and magnetic properties

Wiedwald, U., Fauth, K., Hessler, M., Boyen, H. G., Weigl, F., Hilgendorff, M., Giersig, M., Schütz, G., Ziemann, P., Farle, M.

{ChemPhysChem}, 6(12):2522-2526, 2005 (article)

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[BibTex]

[BibTex]


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Competition between order and phase separation in Au-Ni

Reichert, H., Schöps, A., Ramsteiner, I. B., Bugaev, V. N., Shchyglo, O., Udyansky, A., Dosch, H., Asta, M., Drautz, R., Honkimäki, V.

{Physical Review Letters}, 95(23), 2005 (article)

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[BibTex]

[BibTex]


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Influence of faceting-roughening on triple-junction migration in zinc

Straumal, B. B., Sursaeva, V. G., Gomakova, A. S.

{Zeitschrift f\"ur Metallkunde}, 96(10):1147-1151, 2005 (article)

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[BibTex]

[BibTex]


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Damping of spin dynamics in nanostructures: an ab initio study

Steiauf, D., Fähnle, M.

{Physical Review B}, 72, 2005 (article)

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[BibTex]

[BibTex]


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Test of the hypothesis of transient molten state diffusion for swift-heavy-ion induced mixing

Srivastava, S. K.., Avasthi, D. K., Assmann, W., Wang, Z. G., Kucal, H., Jaquet, E., Carstanjen, H. D., Toulemonde, M.

{Physical Review B}, 71, 2005 (article)

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[BibTex]

[BibTex]


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Hydrogen physisorption in metal-organic porous crystals

Panella, B., Hirscher, M.

{Advanced Materials}, 17(5):538-541, 2005 (article)

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[BibTex]

[BibTex]


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The influence of misorientation deviation on the faceting of \Sigma3 grain boundaries in aluminium

Kogtenkova, O., Straumal, B., Protasova, S., Tsurekawa, S., Watanabe, T.

{Zeitschrift f\"ur Metallkunde}, 96(2):216-219, 2005 (article)

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[BibTex]

[BibTex]


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Metal-oxide interfacial reactions: encapsulation of Pd on TiO2(110)

Fu, Q., Wagner, Th., Olliges, S., Carstanjen, H. D.

{Journal of Physical Chemistry B}, 109, pages: 944-951, 2005 (article)

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[BibTex]

[BibTex]


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Faceting of \Sigma3 and \Sigma9 grain boundaries in Cu-Bi alloys

Straumal, B. B., Polyakov, S. A., Bischoff, E., Gust, W., Baretzky, B.

{Acta Materialia}, 53(2):247-254, 2005 (article)

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[BibTex]

[BibTex]


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Dependence of damage efficiency of ions in diamond on electronic stopping

Friedland, E., Carstanjen, H. D., Myberg, G., Nasr, M. A.

{Nuclear Instruments and Methods in Physics Research B}, 230, pages: 129-132, 2005 (article)

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[BibTex]

[BibTex]


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Volumetric measurement of hydrogen storage in HCl-treated polyaniline and polypyrrole

Panella, B., Kossykh, L., Dettlaff-Weglikowska, U., Hirscher, M., Zerbi, G., Roth, S.

{Synthetic Metals}, 151, pages: 208-210, 2005 (article)

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[BibTex]

[BibTex]


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Permanent magnet alloys based on Sm2Co17; phase evolution in the quinary system Sm-Zr-FeCo-Cu

Stadelmaier, H. H., Goll, D., Kronmüller, H.

{Zeitschrift f\"ur Metallkunde}, 96(1):17-23, 2005 (article)

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[BibTex]

[BibTex]


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Biologically inspired adhesion based surface climbing robots

Menon, C., Sitti, M.

In Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, pages: 2715-2720, 2005 (inproceedings)

pi

[BibTex]

[BibTex]