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2001


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Regularized principal manifolds

Smola, A., Mika, S., Schölkopf, B., Williamson, R.

Journal of Machine Learning Research, 1, pages: 179-209, June 2001 (article)

Abstract
Many settings of unsupervised learning can be viewed as quantization problems - the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised learning. This setting turns out to be closely related to principal curves, the generative topographic map, and robust coding. We explore this connection in two ways: (1) we propose an algorithm for finding principal manifolds that can be regularized in a variety of ways; and (2) we derive uniform convergence bounds and hence bounds on the learning rates of the algorithm. In particular, we give bounds on the covering numbers which allows us to obtain nearly optimal learning rates for certain types of regularization operators. Experimental results demonstrate the feasibility of the approach.

ei

PDF [BibTex]

2001


PDF [BibTex]


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Failure Diagnosis of Discrete Event Systems

Son, HI., Kim, KW., Lee, S.

Journal of Control, Automation and Systems Engineering, 7(5):375-383, May 2001, In Korean (article)

ei

[BibTex]

[BibTex]


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Support vector novelty detection applied to jet engine vibration spectra

Hayton, P., Schölkopf, B., Tarassenko, L., Anuzis, P.

In Advances in Neural Information Processing Systems 13, pages: 946-952, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
A system has been developed to extract diagnostic information from jet engine carcass vibration data. Support Vector Machines applied to novelty detection provide a measure of how unusual the shape of a vibration signature is, by learning a representation of normality. We describe a novel method for Support Vector Machines of including information from a second class for novelty detection and give results from the application to Jet Engine vibration analysis.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm

Still, S., Schölkopf, B., Hepp, K., Douglas, R.

In Advances in Neural Information Processing Systems 13, pages: 741-747, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
To control the walking gaits of a four-legged robot we present a novel neuromorphic VLSI chip that coordinates the relative phasing of the robot's legs similar to how spinal Central Pattern Generators are believed to control vertebrate locomotion [3]. The chip controls the leg movements by driving motors with time varying voltages which are the outputs of a small network of coupled oscillators. The characteristics of the chip's output voltages depend on a set of input parameters. The relationship between input parameters and output voltages can be computed analytically for an idealized system. In practice, however, this ideal relationship is only approximately true due to transistor mismatch and offsets.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Algorithmic Stability and Generalization Performance

Bousquet, O., Elisseeff, A.

In Advances in Neural Information Processing Systems 13, pages: 196-202, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
We present a novel way of obtaining PAC-style bounds on the generalization error of learning algorithms, explicitly using their stability properties. A {\em stable} learner being one for which the learned solution does not change much for small changes in the training set. The bounds we obtain do not depend on any measure of the complexity of the hypothesis space (e.g. VC dimension) but rather depend on how the learning algorithm searches this space, and can thus be applied even when the VC dimension in infinite. We demonstrate that regularization networks possess the required stability property and apply our method to obtain new bounds on their generalization performance.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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The Kernel Trick for Distances

Schölkopf, B.

In Advances in Neural Information Processing Systems 13, pages: 301-307, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as norm-based distances in Hilbert spaces. It turns out that the common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. As well as providing a useful new insight into how these algorithms work, the present work can form the basis for conceiving new algorithms.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Vicinal Risk Minimization

Chapelle, O., Weston, J., Bottou, L., Vapnik, V.

In Advances in Neural Information Processing Systems 13, pages: 416-422, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS) , April 2001 (inproceedings)

Abstract
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or Statistical Regularization. We explain how VRM provides a framework which integrates a number of existing algorithms, such as Parzen windows, Support Vector Machines, Ridge Regression, Constrained Logistic Classifiers and Tangent-Prop. We then show how the approach implies new algorithms for solving problems usually associated with generative models. New algorithms are described for dealing with pattern recognition problems with very different pattern distributions and dealing with unlabeled data. Preliminary empirical results are presented.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Feature Selection for SVMs

Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.

In Advances in Neural Information Processing Systems 13, pages: 668-674, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Occam’s Razor

Rasmussen, CE., Ghahramani, Z.

In Advances in Neural Information Processing Systems 13, pages: 294-300, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
The Bayesian paradigm apparently only sometimes gives rise to Occam's Razor; at other times very large models perform well. We give simple examples of both kinds of behaviour. The two views are reconciled when measuring complexity of functions, rather than of the machinery used to implement them. We analyze the complexity of functions for some linear in the parameter models that are equivalent to Gaussian Processes, and always find Occam's Razor at work.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Pattern Selection Using the Bias and Variance of Ensemble

Shin, H., Cho, S.

Journal of the Korean Institute of Industrial Engineers, 28(1):112-127, March 2001 (article)

Abstract
[Abstract]: A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern “utility index” that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.

ei

[BibTex]

[BibTex]


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Structure and Functionality of a Designed p53 Dimer.

Davison, TS., Nie, X., Ma, W., Lin, Y., Kay, C., Benchimol, S., Arrowsmith, C.

Journal of Molecular Biology, 307(2):605-617, March 2001 (article)

Abstract
P53 is a homotetrameric tumor suppressor protein involved in transcriptional control of genes that regulate cell proliferation and death. In order to probe the role that oligomerization plays in this capacity, we have previously designed and characterized a series of p53 proteins with altered oligomeric states through hydrophilc substitution of residues Met340 or Leu344 in the normally tetrameric oligomerization domain. Although such mutations have little effect on the overall secondary structural content of the oligomerization domain, both solubility and the resistance to thermal denaturation are substantially reduced relative to that of the wild-type domain. Here, we report the design and characterization of a double-mutant p53 with alterations of residues at positions Met340 and Leu344. The double-mutations Met340Glu/Leu344Lys and Met340Gln/Leu344Arg resulted in distinct dimeric forms of the protein. Furthermore, we have verified by NMR structure determination that the double-mutant Met340Gln/Leu344Arg is essentially a "half-tetramer". Analysis of the in vivo activities of full-length p53 oligomeric mutants reveals that while cell-cycle arrest requires tetrameric p53, transcriptional transactivation activity of monomers and dimers retain roughly background and half of the wild-type activity, respectively.

ei

Web [BibTex]

Web [BibTex]


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An Introduction to Kernel-Based Learning Algorithms

Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.

IEEE Transactions on Neural Networks, 12(2):181-201, March 2001 (article)

Abstract
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis

ei

DOI [BibTex]

DOI [BibTex]


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Estimating the support of a high-dimensional distribution.

Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.

Neural Computation, 13(7):1443-1471, March 2001 (article)

Abstract
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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An Improved Training Algorithm for Kernel Fisher Discriminants

Mika, S., Schölkopf, B., Smola, A.

In Proceedings AISTATS, pages: 98-104, (Editors: T Jaakkola and T Richardson), Morgan Kaufman, San Francisco, CA, Artificial Intelligence and Statistics (AISTATS), January 2001 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Nonstationary Signal Classification using Support Vector Machines

Gretton, A., Davy, M., Doucet, A., Rayner, P.

In 11th IEEE Workshop on Statistical Signal Processing, pages: 305-305, 11th IEEE Workshop on Statistical Signal Processing, 2001 (inproceedings)

Abstract
In this paper, we demonstrate the use of support vector (SV) techniques for the binary classification of nonstationary sinusoidal signals with quadratic phase. We briefly describe the theory underpinning SV classification, and introduce the Cohen's group time-frequency representation, which is used to process the non-stationary signals so as to define the classifier input space. We show that the SV classifier outperforms alternative classification methods on this processed data.

ei

PostScript [BibTex]

PostScript [BibTex]


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Enhanced User Authentication through Typing Biometrics with Artificial Neural Networks and K-Nearest Neighbor Algorithm

Wong, FWMH., Supian, ASM., Ismail, AF., Lai, WK., Ong, CS.

In 2001 (inproceedings)

ei

[BibTex]

[BibTex]


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Predicting the Nonlinear Dynamics of Biological Neurons using Support Vector Machines with Different Kernels

Frontzek, T., Lal, TN., Eckmiller, R.

In Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC, 2, pages: 1492-1497, Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC, 2001 (inproceedings)

Abstract
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian, polynomial and tanh-kernels to learn and predict the nonlinear dynamics of single biological neurons. We show that SVMs for regression learn the dynamics of the pyloric dilator neuron of the australian crayfish, and we determine the optimal SVM parameters with regard to the test error. Compared to conventional RBF networks and MLPs, SVMs with gaussian kernels learned faster and performed a better iterated one-step-ahead prediction with regard to training and test error. From a biological point of view SVMs are especially better in predicting the most important part of the dynamics, where the membranpotential is driven by superimposed synaptic inputs to the threshold for the oscillatory peak.

ei

PDF [BibTex]

PDF [BibTex]


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Computationally Efficient Face Detection

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

In Computer Vision, ICCV 2001, vol. 2, (73):695-700, IEEE, 8th International Conference on Computer Vision, 2001 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Design and Verification of Supervisory Controller of High-Speed Train

Yoo, SP., Lee, DY., Son, HI.

In IEEE International Symposium on Industrial Electronics, pages: 1290-1295, IEEE Operations Center, Piscataway, NJ, USA, IEEE International Symposium on Industrial Electronics (ISIE), 2001 (inproceedings)

Abstract
A high-level controller, supervisory controller, is required to monitor, control, and diagnose the low-level controllers of the high-speed train. The supervisory controller controls low-level controllers by monitoring input and output signals, events, and the high-speed train can be modeled as a discrete event system (DES). The high-speed train is modeled with automata, and the high-level control specification is defined. The supervisory controller is designed using the high-speed train model and the control specification. The designed supervisory controller is verified and evaluated with simulation using a computer-aided software engineering (CASE) tool, Object GEODE

ei

Web DOI [BibTex]

Web DOI [BibTex]


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The psychometric function: II. Bootstrap-based confidence intervals and sampling

Wichmann, F., Hill, N.

Perception and Psychophysics, 63 (8), pages: 1314-1329, 2001 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Towards Learning Path Planning for Solving Complex Robot Tasks

Frontzek, T., Lal, TN., Eckmiller, R.

In Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna, pages: 943-950, Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna, 2001 (inproceedings)

Abstract
For solving complex robot tasks it is necessary to incorporate path planning methods that are able to operate within different high-dimensional configuration spaces containing an unknown number of obstacles. Based on Advanced A*-algorithm (AA*) using expansion matrices instead of a simple expansion logic we propose a further improvement of AA* enabling the capability to learn directly from sample planning tasks. This is done by inserting weights into the expansion matrix which are modified according to a special learning rule. For an examplary planning task we show that Adaptive AA* learns movement vectors which allow larger movements than the initial ones into well-defined directions of the configuration space. Compared to standard approaches planning times are clearly reduced.

ei

PDF [BibTex]

PDF [BibTex]


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Learning to predict the leave-one-out error of kernel based classifiers

Tsuda, K., Rätsch, G., Mika, S., Müller, K.

In International Conference on Artificial Neural Networks, ICANN'01, (LNCS 2130):331-338, (Editors: G. Dorffner, H. Bischof and K. Hornik), International Conference on Artificial Neural Networks, ICANN'01, 2001 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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A kernel approach for vector quantization with guaranteed distortion bounds

Tipping, M., Schölkopf, B.

In Artificial Intelligence and Statistics, pages: 129-134, (Editors: T Jaakkola and T Richardson), Morgan Kaufmann, San Francisco, CA, USA, 8th International Conference on Artificial Intelligence and Statistics (AI and STATISTICS), 2001 (inproceedings)

ei

[BibTex]

[BibTex]


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The psychometric function: I. Fitting, sampling and goodness-of-fit

Wichmann, F., Hill, N.

Perception and Psychophysics, 63 (8), pages: 1293-1313, 2001 (article)

Abstract
The psychometric function relates an observer'sperformance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function'sparameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (or lapses ). We show that failure to account for this can lead to serious biases in estimates of the psychometric function'sparameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditional X^2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods

ei

PDF [BibTex]

PDF [BibTex]


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Tracking a Small Set of Experts by Mixing Past Posteriors

Bousquet, O., Warmuth, M.

In Proceedings of the 14th Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science, 2111, pages: 31-47, Proceedings of the 14th Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science, 2001 (inproceedings)

Abstract
In this paper, we examine on-line learning problems in which the target concept is allowed to change over time. In each trial a master algorithm receives predictions from a large set of $n$ experts. Its goal is to predict almost as well as the best sequence of such experts chosen off-line by partitioning the training sequence into $k+1$ sections and then choosing the best expert for each section. We build on methods developed by Herbster and Warmuth and consider an open problem posed by Freund where the experts in the best partition are from a small pool of size $m$. Since $k>>m$ the best expert shifts back and forth between the experts of the small pool. We propose algorithms that solve this open problem by mixing the past posteriors maintained by the master algorithm. We relate the number of bits needed for encoding the best partition to the loss bounds of the algorithms. Instead of paying $\log n$ for choosing the best expert in each section we first pay $\log {n\choose m}$ bits in the bounds for identifying the pool of $m$ experts and then $\log m$ bits per new section. In the bounds we also pay twice for encoding the boundaries of the sections.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines

Frontzek, T., Lal, TN., Eckmiller, R.

In Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001), pages: 390-398, Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001), 2001 (inproceedings)

Abstract
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We show that SVMs for regression learn the dynamics of the pyloric dilator neuron of the australian crayfish, and we determine the optimal SVM parameters with regard to the test error. Compared to conventional RBF networks, SVMs learned faster and performed a better iterated one-step-ahead prediction with regard to training and test error. From a biological point of view SVMs are especially better in predicting the most important part of the dynamics, where the membranpotential is driven by superimposed synaptic inputs to the threshold for the oscillatory peak.

ei

PDF [BibTex]

PDF [BibTex]


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Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Lawrence, N., Schölkopf, B.

In 18th International Conference on Machine Learning, pages: 306-313, (Editors: CE Brodley and A Pohoreckyj Danyluk), Morgan Kaufmann , San Fransisco, CA, USA, 18th International Conference on Machine Learning (ICML), 2001 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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A Generalized Representer Theorem

Schölkopf, B., Herbrich, R., Smola, A.

In Lecture Notes in Computer Science, Vol. 2111, (2111):416-426, LNCS, (Editors: D Helmbold and R Williamson), Springer, Berlin, Germany, Annual Conference on Computational Learning Theory (COLT/EuroCOLT), 2001 (inproceedings)

ei

[BibTex]

[BibTex]


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Unsupervised Segmentation and Classification of Mixtures of Markovian Sources

Seldin, Y., Bejerano, G., Tishby, N.

In The 33rd Symposium on the Interface of Computing Science and Statistics (Interface 2001 - Frontiers in Data Mining and Bioinformatics), pages: 1-15, 33rd Symposium on the Interface of Computing Science and Statistics (Interface - Frontiers in Data Mining and Bioinformatics), 2001 (inproceedings)

Abstract
We describe a novel algorithm for unsupervised segmentation of sequences into alternating Variable Memory Markov sources, first presented in [SBT01]. The algorithm is based on competitive learning between Markov models, when implemented as Prediction Suffix Trees [RST96] using the MDL principle. By applying a model clustering procedure, based on rate distortion theory combined with deterministic annealing, we obtain a hierarchical segmentation of sequences between alternating Markov sources. The method is applied successfully to unsupervised segmentation of multilingual texts into languages where it is able to infer correctly both the number of languages and the language switching points. When applied to protein sequence families (results of the [BSMT01] work), we demonstrate the method‘s ability to identify biologically meaningful sub-sequences within the proteins, which correspond to signatures of important functional sub-units called domains. Our approach to proteins classification (through the obtained signatures) is shown to have both conceptual and practical advantages over the currently used methods.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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The control structure of artificial creatures

Zhou, D., Dai, R.

Artificial Life and Robotics, 5(3), 2001, invited article (article)

ei

Web [BibTex]

Web [BibTex]


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Support Vector Regression for Black-Box System Identification

Gretton, A., Doucet, A., Herbrich, R., Rayner, P., Schölkopf, B.

In 11th IEEE Workshop on Statistical Signal Processing, pages: 341-344, IEEE Signal Processing Society, Piscataway, NY, USA, 11th IEEE Workshop on Statistical Signal Processing, 2001 (inproceedings)

Abstract
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. These methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling the behaviour of a hydraulic robot arm, and show that SVR improves on previously published results.

ei

PostScript [BibTex]

PostScript [BibTex]


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Markovian domain fingerprinting: statistical segmentation of protein sequences

Bejerano, G., Seldin, Y., Margalit, H., Tishby, N.

Bioinformatics, 17(10):927-934, 2001 (article)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources

Seldin, Y., Bejerano, G., Tishby, N.

In In the proceeding of the 18th International Conference on Machine Learning (ICML 2001), pages: 513-520, 18th International Conference on Machine Learning (ICML), 2001 (inproceedings)

Abstract
We present a novel information theoretic algorithm for unsupervised segmentation of sequences into alternating Variable Memory Markov sources. The algorithm is based on competitive learning between Markov models, when implemented as Prediction Suffix Trees (Ron et al., 1996) using the MDL principle. By applying a model clustering procedure, based on rate distortion theory combined with deterministic annealing, we obtain a hierarchical segmentation of sequences between alternating Markov sources. The algorithm seems to be self regulated and automatically avoids over segmentation. The method is applied successfully to unsupervised segmentation of multilingual texts into languages where it is able to infer correctly both the number of languages and the language switching points. When applied to protein sequence families, we demonstrate the method‘s ability to identify biologically meaningful sub-sequences within the proteins, which correspond to important functional sub-units called domains.

ei

PDF [BibTex]

PDF [BibTex]


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Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation

Cheng, Y., Fu, Q., Gu, L., Li, S., Schölkopf, B., Zhang, H.

In Proceedings Computer Vision, 2001, Vol. 2, pages: 674-679, IEEE Computer Society, 8th International Conference on Computer Vision (ICCV), 2001 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]

1996


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Quality Prediction of Steel Products using Neural Networks

Shin, H., Jhee, W.

In Proc. of the Korean Expert System Conference, pages: 112-124, Korean Expert System Society Conference, November 1996 (inproceedings)

ei

[BibTex]

1996


[BibTex]


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Comparison of view-based object recognition algorithms using realistic 3D models

Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., Vetter, T.

In Artificial Neural Networks: ICANN 96, LNCS, vol. 1112, pages: 251-256, Lecture Notes in Computer Science, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996 (inproceedings)

Abstract
Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of the objects. Classification performance is assessed using a high number of images generated by a computer graphics system under precisely controlled conditions. Training- and test-images show a set of 25 realistic three-dimensional models of chairs from viewing directions spread over the upper half of the viewing sphere. The percentage of correct identification of all 25 objects is measured.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Incorporating invariances in support vector learning machines

Schölkopf, B., Burges, C., Vapnik, V.

In Artificial Neural Networks: ICANN 96, LNCS vol. 1112, pages: 47-52, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996, volume 1112 of Lecture Notes in Computer Science (inproceedings)

Abstract
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A practical Monte Carlo implementation of Bayesian learning

Rasmussen, CE.

In Advances in Neural Information Processing Systems 8, pages: 598-604, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 datalimited tasks from real world domains.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Gaussian Processes for Regression

Williams, CKI., Rasmussen, CE.

In Advances in neural information processing systems 8, pages: 514-520, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

ei

PDF Web [BibTex]

PDF Web [BibTex]