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2017


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A Comparison of Autoregressive Hidden Markov Models for Multimodal Manipulations With Variable Masses

Kroemer, O., Peters, J.

IEEE Robotics and Automation Letters, 2(2):1101-1108, 2017 (article)

ei

DOI [BibTex]

2017


DOI [BibTex]


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Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration

Maeda, G., Ewerton, M., Neumann, G., Lioutikov, R., Peters, J.

International Journal of Robotics Research, 36(13-14):1579-1594, 2017, Special Issue on the Seventeenth International Symposium on Robotics Research (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Phase-coded Aperture Camera with Programmable Optics

Chen, J., Hirsch, M., Heintzmann, R., Eberhardt, B., Lensch, H. P. A.

Electronic Imaging, 2017(17):70-75, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Selective hydrogen isotope separation via breathing transition in MIL-53(Al)

Kim, J. Y., Zhang, L., Balderas-Xicohténcatl, R., Park, J., Hirscher, M., Moon, H. R., Oh, H.

{Journal of the American Chemical Society}, 139(49):17743-17746, American Chemical Society, Washington, DC, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Efficient synthesis for large-scale production and characterization for hydrogen storage of ligand exchanged MOF-74/174/184-M (M\textequalsMg2+, Ni2+)

Oh, H., Maurer, S., Balderas-Xicohténcatl, R., Arnold, L., Magdysyuk, O. V., Schütz, G., Müller, U., Hirscher, M.

{International Journal of Hydrogen Energy}, 42(2):1027-1035, Elsevier, Amsterdam, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, 2017 (article)

Abstract
Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. Towards this goal, we first provide a taxonomy to classify each approach and then analyze the performance of the state-of-the-art on several challenging benchmarking datasets including KITTI, ISPRS, MOT and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we will also provide an interactive platform which allows to navigate topics and methods, and provides additional information and project links for each paper.

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pdf Project Page Project Page [BibTex]


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A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.05435, 2017 (article)

Abstract
We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsule robots inside the GI tract using only visual information captured by a mono camera integrated to the robot. The proposed system is a 23-layer deep convolutional neural network (CNN) that is capable to estimate the pose of the robot in real time using a standard CPU. The dataset for the evaluation of the system was recorded inside a surgical human stomach model with realistic surface texture, softness, and surface liquid properties so that the pre-trained CNN architecture can be transferred confidently into a real endoscopic scenario. An average error of 7.1% and 3.4% for translation and rotation has been obtained, respectively. The results accomplished from the experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images performs accurately inside the GI tract and is robust to various challenges posed by reflection distortions, lens imperfections, vignetting, noise, motion blur, low resolution, and lack of unique landmarks to track.

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link (url) Project Page [BibTex]


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Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

Wahl, N., Hennig, P., Wieser, H. P., Bangert, M.

Physics in Medicine & Biology, 62(14):5790-5807, 2017 (article)

Abstract
The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ##IMG## [http://ej.iop.org/images/0031-9155/62/14/5790/pmbaa6ec5ieqn001.gif] {$\leqslant {5}$} min). The resulting standard deviation (expectation value) of dose show average global ##IMG## [http://ej.iop.org/images/0031-9155/62/14/5790/pmbaa6ec5ieqn002.gif] {$\gamma_{{3}\% / {3}~{\rm mm}}$} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.

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link (url) [BibTex]

link (url) [BibTex]


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Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.

ArXiv e-prints, 2017 (article)

Abstract
Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

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link (url) Project Page [BibTex]


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Analytical probabilistic modeling of RBE-weighted dose for ion therapy

Wieser, H., Hennig, P., Wahl, N., Bangert, M.

Physics in Medicine and Biology (PMB), 62(23):8959-8982, 2017 (article)

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link (url) [BibTex]

link (url) [BibTex]


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On Maximum Entropy and Inference

Gresele, L., Marsili, M.

Entropy, 19(12):article no. 642, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human-Robot Assembly Task

Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E.

International Journal of Social Robotics, 9(1):63-86, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Non-parametric Policy Search with Limited Information Loss

van Hoof, H., Neumann, G., Peters, J.

Journal of Machine Learning Research , 18(73):1-46, 2017 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Stability of Controllers for Gaussian Process Dynamics

Vinogradska, J., Bischoff, B., Nguyen-Tuong, D., Peters, J.

Journal of Machine Learning Research, 18(100):1-37, 2017 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Two-sample tests for large random graphs using network statistics

Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U.

In Conference on Computational Learning Theory (COLT), Conference on Computational Learning Theory (COLT), 2017 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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SUV-quantification of physiological lung tissue in an integrated PET/MR-system: Impact of lung density and bone tissue

Seith, F., Schmidt, H., Gatidis, S., Bezrukov, I., Schraml, C., Pfannenberg, C., la Fougère, C., Nikolaou, K., Schwenzer, N.

PLOS ONE, 12(5):1-13, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Corrosion-protected hybrid nanoparticles

Jeong, H., Alarcón-Correa, M., Mark, A. G., Son, K., Lee, T., Fischer, P.

{Advanced Science}, 4(12), Wiley-VCH, Weinheim, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Investigation of the Dzyaloshinskii-Moriya interaction and room temperature skyrmions in W/CoFeB/MgO thin films and microwires

Jaiswal, S., Litzius, K., Lemesh, I., Büttner, F., Finizio, S., Raabe, J., Weigand, M., Lee, K., Langer, J., Ocker, B., Jakob, G., Beach, G. S. D., Kläui, M.

{Applied Physics Letters}, 111(2), American Institute of Physics, Melville, NY, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Ultrafast demagnetization after femtosecond laser pulses: Transfer of angular momentum from the electronic system to magnetoelastic spin-phonon modes

Fähnle, M., Tsatsoulis, T., Illg, C., Haag, M., Müller, B. Y., Zhang, L.

{Journal of Superconductivity and Novel Magnetism}, 30(5):1381-1387, Springer Science + Business Media B.V., New York, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic behavior of single chain magnets in metal organic frameworks CPO-27-Co

Son, K., Goering, E., Hirscher, M., Oh, H.

{Journal of Nanoscience and Nanotechnology}, 17(10):7541-7546, American Scientific Publishers, Stevenson Ranch, Calif., 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Switching by domain-wall automotion in asymmetric ferromagnetic rings

Mawass, M., Richter, K., Bisig, A., Reeve, R. M., Krüger, B., Weigand, M., Stoll, H., Krone, A., Kronast, F., Schütz, G., Kläui, M.

{Physical Review Applied}, 7(4), American Physical Society, College Park, Md. [u.a.], 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Pattern Generation for Walking on Slippery Terrains

Khadiv, M., Moosavian, S. A. A., Herzog, A., Righetti, L.

In 2017 5th International Conference on Robotics and Mechatronics (ICROM), Iran, August 2017 (inproceedings)

Abstract
In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this for- mulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.

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link (url) [BibTex]

link (url) [BibTex]


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A neutral atom moving in an external magnetic field does not feel a Lorentz force

Fähnle, M.

{American Journal of Modern Physics}, 6(6):153-155, Science Publishing Group, New York, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Temperature-dependent first-order reversal curve measurements on unusually hard magnetic low-temperature phase of MnBi

Muralidhar, S., Gräfe, J., Chen, Y., Etter, M., Gregori, G., Ener, S., Sawatzki, S., Hono, K., Gutfleisch, O., Kronmüller, H., Schütz, G., Goering, E. J.

{Physical Review B}, 95(2), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Smooth and rapid microwave synthesis of MIL-53(Fe) including superparamagnetic \textlessgamma\textgreater-Fe2O3 nanoparticles

Wengert, S., Albrecht, J., Ruoß, S., Stahl, C., Schütz, G., Schäfer, R.

{Journal of Magnetism and Magnetic Materials}, 444, pages: 168-172, NH, Elsevier, Amsterdam, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Characterization and differentiation of rock varnish types from different environments by microanalytical techniques

Macholdt, D. S., Jochum, K. P., Pöhlker, C., Arangio, A., Förster, J., Stoll, B., Weis, U., Weber, B., Müller, M., Kappl, M., Shiraiwa, M., Kilcoyne, A. L. D., Weigand, M., Scholz, D., Haug, G. H., Al-Amri, A., Andreae, M. O.

{Chemical Geology}, 459, pages: 91-118, Elsevier, Amsterdam, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Skyrmion Hall effect revealed by direct time-resolved X-ray microscopy

Litzius, K., Lemesh, I., Krüger, B., Bassirian, P., Caretta, L., Richter, K., Büttner, F., Sato, K., Tretiakov, O. A., Förster, J., Reeve, R. M., Weigand, M., Bykova, I., Stoll, H., Schütz, G., Beach, G. S. D., Kläui, M.

{Nature Physics}, 13(2):170-175, Nature Pub. Group, London, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Is Growing Good for Learning?

Heim, Steve, Spröwitz, Alexander

In Proceedings of the 8th International Symposium on Adaptive Motion of Animals and Machines AMAM2017, Hokkaido, Japan, 2017 (inproceedings)

[BibTex]

[BibTex]


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Comment on magnonic black holes

Fähnle, M., Schütz, G.

{Journal of Magnetism and Magnetic Materials}, 444, pages: 146-146, NH, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Cr-Substitution in Ba2In2O5 \mbox⋅ (H2O)x (x \textequals 0.16, 0.74)

Yoon, S., Son, K., Hagemann, H., Widenmeyer, M., Weidenkaff, A.

{Solid State Sciences}, 73, pages: 1-6, Elsevier Masson SAS, Paris, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Comment on half-integer quantum numbers for the total angular momentum of photons in light beams with finite lateral extensions

Fähnle, M.

{American Journal of Modern Physics}, 6(5):88-90, Science Publishing Group, New York, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Advanced magneto-optical Kerr effect measurements of superconductors at low temperatures

Stahl, C., Gräfe, J., Ruoß, S., Zahn, P., Bayer, J., Simmendinger, J., Schütz, G., Albrecht, J.

{AIP Advances}, 7(10), 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Unifying ultrafast demagnetization and intrinsic Gilbert damping in Co/Ni bilayers with electronic relaxation near the Fermi surface

Zhang, W., He, W., Zhang, X.-Q., Cheng, Z.-H., Teng, J., Fähnle, M.

{Physical Review B}, 96(22), American Physical Society, Woodbury, NY, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Influence of the skin barrier on the penetration of topically-applied dexamethasone probed by soft X-ray spectromicroscopy

Yamamoto, K., Klossek, A., Flesch, R., Rancan, F., Weigand, M., Bykova, I., Bechtel, M., Ahlberg, S., Vogt, A., Blume-Peytavi, U., Schrade, P., Bachmann, S., Hedtrich, S., Schäfer-Korting, M., Rühl, E.

{European Journal of Pharmaceutics and Biopharmaceutics}, 118, pages: 30-37, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Capture of heavy hydrogen isotopes in a metal-organic framework with active Cu(I) sites

Weinrauch, I., Savchenko, I., Denysenko, D., Souliou, S. M., Kim, H., Le Tacon, M., Daemen, L. L., Cheng, Y., Mavrandonakis, A., Ramirez-Cuesta, A. J., Volkmer, D., Schütz, G., Hirscher, M., Heine, T.

{Nature Communications}, 8, Nature Publishing Group, London, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Multiscale simulations of topological transformations in magnetic-skyrmion spin structures

De Lucia, A., Litzius, K., Krüger, B., Tretiakov, O. A., Kläui, M.

{Physical Review B}, 96(2), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Unexpectedly marginal effect of electronic correlations on ultrafast demagnetization after femtosecond laser-pulse excitation

Weng, W., Huang, Haonan, Briones Paz, J. Z., Teeny, N., Müller, B. Y., Haag, M., Kuhn, T., Fähnle, M.

{Physical Review B}, 95(22), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Black manganese-rich crusts on a Gothic cathedral

Macholdt, D. S., Herrmann, S., Jochum, K. P., Kilcoyne, A. L. D., Laubscher, T., Pfisterer, H. K., Pöhlker, C., Schwager, B., Weber, B., Weigand, M., Domke, K. F., Andreae, M. O.

{Atmospheric Environment}, 171, pages: 205-220, Elsevier, Amsterdam [u.a.], 2017 (article)

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DOI [BibTex]

DOI [BibTex]

2005


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Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

ei

PDF PostScript PDF [BibTex]

2005


PDF PostScript PDF [BibTex]


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Kernel ICA for Large Scale Problems

Jegelka, S., Gretton, A., Achlioptas, D.

In pages: -, NIPS Workshop on Large Scale Kernel Machines, December 2005 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract
We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

ei

PDF [BibTex]

PDF [BibTex]


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Training Support Vector Machines with Multiple Equality Constraints

Kienzle, W., Schölkopf, B.

In Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720, pages: 182-193, (Editors: JG Carbonell and J Siekmann), Springer, Berlin, Germany, ECML, November 2005 (inproceedings)

Abstract
In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 3734, pages: 63-78, (Editors: S Jain and H-U Simon and E Tomita), Springer, Berlin, Germany, 16th International Conference ALT, October 2005 (inproceedings)

Abstract
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on {methodname} do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron

Kowalczyk, A., Chapelle, O.

In Algorithmic Learning Theory: 16th International Conference, pages: 78-92, Algorithmic Learning Theory, October 2005 (inproceedings)

Abstract
This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property of data sets and is quite distinct from overfitting of a training data. Moreover, the analysis leads to a specification of some machine learning procedures which can overcome anti-learning and generate ma- chines able to classify training and test data consistently.

ei

PDF [BibTex]

PDF [BibTex]


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Selective integration of multiple biological data for supervised network inference

Kato, T., Tsuda, K., Asai, K.

Bioinformatics, 21(10):2488 , October 2005 (article)

ei

PDF [BibTex]

PDF [BibTex]