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2012


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Gradient Weights help Nonparametric Regressors

Kpotufe, S., Boularias, A.

In Advances in Neural Information Processing Systems 25, pages: 2870-2878, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF [BibTex]

2012

PDF [BibTex]


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A Blind Deconvolution Approach for Pseudo CT Prediction from MR Image Pairs

Hirsch, M., Hofmann, M., Mantlik, F., Pichler, B., Schölkopf, B., Habeck, M.

In 19th IEEE International Conference on Image Processing (ICIP) , pages: 2953 -2956, IEEE, ICIP, 2012 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Mining correlated loci at a genome-wide scale

Velkov, V.

Eberhard Karls Universität Tübingen, Germany, 2012 (mastersthesis)

[BibTex]

[BibTex]


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A mixed model approach for joint genetic analysis of alternatively spliced transcript isoforms using RNA-Seq data

Rakitsch, B., Lippert, C., Topa, H., Borgwardt, KM., Honkela, A., Stegle, O.

In 2012 (inproceedings) Submitted

Web [BibTex]

Web [BibTex]


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The PET Performance Measurements of A Next Generation Dedicated Small Animal PET/MR Scanner

Liu, C., Hossain, M., Bezrukov, I., Wehrl, H., Kolb, A., Judenhofer, M., Pichler, B.

World Molecular Imaging Congress (WMIC), 2012 (poster)

[BibTex]

[BibTex]


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Evaluation of marginal likelihoods via the density of states

Habeck, M.

In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012) , 22, pages: 486-494, (Editors: N Lawrence and M Girolami), JMLR: W&CP 22, AISTATS, 2012 (inproceedings)

Abstract
Bayesian model comparison involves the evaluation of the marginal likelihood, the expectation of the likelihood under the prior distribution. Typically, this high-dimensional integral over all model parameters is approximated using Markov chain Monte Carlo methods. Thermodynamic integration is a popular method to estimate the marginal likelihood by using samples from annealed posteriors. Here we show that there exists a robust and flexible alternative. The new method estimates the density of states, which counts the number of states associated with a particular value of the likelihood. If the density of states is known, computation of the marginal likelihood reduces to a one- dimensional integral. We outline a maximum likelihood procedure to estimate the density of states from annealed posterior samples. We apply our method to various likelihoods and show that it is superior to thermodynamic integration in that it is more flexible with regard to the annealing schedule and the family of bridging distributions. Finally, we discuss the relation of our method with Skilling's nested sampling.

PDF [BibTex]

PDF [BibTex]


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Distributed multisensory signals acquisition and analysis in dyadic interactions

Tawari, A., Tran, C., Doshi, A., Zander, TO.

In Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems Extended Abstracts, pages: 2261-2266, (Editors: JA Konstan and EH Chi and K Höök), ACM, New York, NY, USA, CHI, 2012 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Measuring Cognitive Load by means of EEG-data - how detailed is the picture we can get?

Scharinger, C., Cierniak, G., Walter, C., Zander, TO., Gerjets, P.

In Meeting of the EARLI SIG 22 Neuroscience and Education, 2012 (inproceedings)

[BibTex]

[BibTex]


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Optimal kernel choice for large-scale two-sample tests

Gretton, A., Sriperumbudur, B., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K.

In Advances in Neural Information Processing Systems 25, pages: 1214-1222, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Measurement and calibration of noise bias in weak lensing galaxy shape estimation

Kacprzak, T., Zuntz, J., Rowe, B., Bridle, S., Refregier, A., Amara, A., Voigt, L., Hirsch, M.

Monthly Notices of the Royal Astronomical Society, 427(4):2711-2722, Oxford University Press, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Image analysis for cosmology: results from the GREAT10 Galaxy Challenge

Kitching, T. D., Balan, S. T., Bridle, S., Cantale, N., Courbin, F., Eifler, T., Gentile, M., Gill, M. S. S., Harmeling, S., Heymans, C., others,

Monthly Notices of the Royal Astronomical Society, 423(4):3163-3208, Oxford University Press, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples

Ben-David, S., Urner, R.

In Algorithmic Learning Theory - 23rd International Conference, 7568, pages: 139-153, Lecture Notes in Computer Science, (Editors: Bshouty, NH. and Stoltz, G and Vayatis, N and Zeugmann, T), Springer Berlin Heidelberg, ALT, 2012 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Domain Adaptation–Can Quantity compensate for Quality?

Ben-David, S., Shalev-Shwartz, S., Urner, R.

In International Symposium on Artificial Intelligence and Mathematics, ISAIM, 2012 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Learning from Weak Teachers

Urner, R., Ben-David, S., Shamir, O.

In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, 22, pages: 1252-1260, (Editors: Lawrence, N. and Girolami, M.), JMLR, AISTATS, 2012 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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First SN Discoveries from the Dark Energy Survey

Abbott, T., Abdalla, F., Achitouv, I., Ahn, E., Aldering, G., Allam, S., Alonso, D., Amara, A., Annis, J., Antonik, M., others,

The Astronomer's Telegram, 4668, pages: 1, 2012 (article)

[BibTex]

[BibTex]


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A sensorimotor paradigm for Bayesian model selection

Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 6(291):1-16, October 2012 (article)

Abstract
Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects’ choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.

DOI [BibTex]

DOI [BibTex]


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Adaptive Coding of Actions and Observations

Ortega, PA, Braun, DA

pages: 1-4, NIPS Workshop on Information in Perception and Action, December 2012 (conference)

Abstract
The application of expected utility theory to construct adaptive agents is both computationally intractable and statistically questionable. To overcome these difficulties, agents need the ability to delay the choice of the optimal policy to a later stage when they have learned more about the environment. How should agents do this optimally? An information-theoretic answer to this question is given by the Bayesian control rule—the solution to the adaptive coding problem when there are not only observations but also actions. This paper reviews the central ideas behind the Bayesian control rule.

link (url) [BibTex]

link (url) [BibTex]


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Risk-Sensitivity in Bayesian Sensorimotor Integration

Grau-Moya, J, Ortega, PA, Braun, DA

PLoS Computational Biology, 8(9):1-7, sep 2012 (article)

Abstract
Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.

DOI [BibTex]

DOI [BibTex]


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Free Energy and the Generalized Optimality Equations for Sequential Decision Making

Ortega, PA, Braun, DA

pages: 1-10, 10th European Workshop on Reinforcement Learning (EWRL), July 2012 (conference)

Abstract
The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees that include both adversarial and stochastic environments. We derive generalized sequential optimality equations that not only include the Bellman optimality equations as a limit case, but also lead to well-known decision-rules such as Expectimax, Minimax and Expectiminimax. We show how these decision-rules can be derived from a single free energy principle that assigns a resource parameter to each node in the decision tree. These resource parameters express a concrete computational cost that can be measured as the amount of samples that are needed from the distribution that belongs to each node. The free energy principle therefore provides the normative basis for generalized optimality equations that account for both adversarial and stochastic environments.

link (url) [BibTex]

link (url) [BibTex]

2001


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Pattern Selection Using the Bias and Variance of Ensemble

Shin, H., Cho, S.

In Proc. of the Korean Data Mining Conference, pages: 56-67, Korean Data Mining Conference, December 2001 (inproceedings)

[BibTex]

2001

[BibTex]


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Separation of post-nonlinear mixtures using ACE and temporal decorrelation

Ziehe, A., Kawanabe, M., Harmeling, S., Müller, K.

In ICA 2001, pages: 433-438, (Editors: Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski), Third International Workshop on Independent Component Analysis and Blind Signal Separation, December 2001 (inproceedings)

Abstract
We propose an efficient method based on the concept of maximal correlation that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. For this we apply the Alternating Conditional Expectation (ACE) algorithm – a powerful technique from nonparametric statistics – to approximately invert the (post-)nonlinear functions. Interestingly, in the framework of the ACE method convergence can be proven and in the PNL BSS scenario the optimal transformation found by ACE will coincide with the desired inverse functions. After the nonlinearities have been removed by ACE, temporal decorrelation (TD) allows us to recover the source signals. An excellent performance underlines the validity of our approach and demonstrates the ACE-TD method on realistic examples.

PDF [BibTex]

PDF [BibTex]


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Perception of Planar Shapes in Depth

Wichmann, F., Willems, B., Rosas, P., Wagemans, J.

Journal of Vision, 1(3):176, First Annual Meeting of the Vision Sciences Society (VSS), December 2001 (poster)

Abstract
We investigated the influence of the perceived 3D-orientation of planar elliptical shapes on the perception of the shapes themselves. Ellipses were projected onto the surface of a sphere and subjects were asked to indicate if the projected shapes looked as if they were a circle on the surface of the sphere. The image of the sphere was obtained from a real, (near) perfect sphere using a highly accurate digital camera (real sphere diameter 40 cm; camera-to-sphere distance 320 cm; for details see Willems et al., Perception 29, S96, 2000; Photometrics SenSys 400 digital camera with Rodenstock lens, 12-bit linear luminance resolution). Stimuli were presented monocularly on a carefully linearized Sony GDM-F500 monitor keeping the scene geometry as in the real case (sphere diameter on screen 8.2 cm; viewing distance 66 cm). Experiments were run in a darkened room using a viewing tube to minimize, as far as possible, extraneous monocular cues to depth. Three different methods were used to obtain subjects' estimates of 3D-shape: the method of adjustment, temporal 2-alternative forced choice (2AFC) and yes/no. Several results are noteworthy. First, mismatch between perceived and objective slant tended to decrease with increasing objective slant. Second, the variability of the settings, too, decreased with increasing objective slant. Finally, we comment on the results obtained using different psychophysical methods and compare our results to those obtained using a real sphere and binocular vision (Willems et al.).

Web DOI [BibTex]

Web DOI [BibTex]


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Anabolic and Catabolic Gene Expression Pattern Analysis in Normal Versus Osteoarthritic Cartilage Using Complementary DNA-Array Technology

Aigner, T., Zien, A., Gehrsitz, A., Gebhard, P., McKenna, L.

Arthritis and Rheumatism, 44(12):2777-2789, December 2001 (article)

Web [BibTex]

Web [BibTex]


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Nonlinear blind source separation using kernel feature spaces

Harmeling, S., Ziehe, A., Kawanabe, M., Blankertz, B., Müller, K.

In ICA 2001, pages: 102-107, (Editors: Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski), Third International Workshop on Independent Component Analysis and Blind Signal Separation, December 2001 (inproceedings)

Abstract
In this work we propose a kernel-based blind source separation (BSS) algorithm that can perform nonlinear BSS for general invertible nonlinearities. For our kTDSEP algorithm we have to go through four steps: (i) adapting to the intrinsic dimension of the data mapped to feature space F, (ii) finding an orthonormal basis of this submanifold, (iii) mapping the data into the subspace of F spanned by this orthonormal basis, and (iv) applying temporal decorrelation BSS (TDSEP) to the mapped data. After demixing we get a number of irrelevant components and the original sources. To find out which ones are the components of interest, we propose a criterion that allows to identify the original sources. The excellent performance of kTDSEP is demonstrated in experiments on nonlinearly mixed speech data.

PDF [BibTex]

PDF [BibTex]


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Pattern Selection for ‘Regression’ using the Bias and Variance of Ensemble Network

Shin, H., Cho, S.

In Proc. of the Korean Institute of Industrial Engineers Conference, pages: 10-19, Korean Industrial Engineers Conference, November 2001 (inproceedings)

[BibTex]

[BibTex]


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Kernel Methods for Extracting Local Image Semantics

Bradshaw, B., Schölkopf, B., Platt, J.

(MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Web [BibTex]

Web [BibTex]


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Pattern Selection for ‘Classification’ using the Bias and Variance of Ensemble Neural Network

Shin, H., Cho, S.

In Proc. of the Korea Information Science Conference, pages: 307-309, Korea Information Science Conference, October 2001, Best Paper Award (inproceedings)

[BibTex]

[BibTex]


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Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

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

IEEE Transactions on Information Theory, 47(6):2516-2532, September 2001 (article)

Abstract
We derive new bounds for the generalization error of kernel machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs make use of a viewpoint that is apparently novel in the field of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly infinite-dimensional unit ball in feature space into a finite-dimensional space. The covering numbers of the class are then determined via the entropy numbers of the operator. These numbers, which characterize the degree of compactness of the operator can be bounded in terms of the eigenvalues of an integral operator induced by the kernel function used by the machine. As a consequence, we are able to theoretically explain the effect of the choice of kernel function on the generalization performance of support vector machines.

DOI [BibTex]

DOI [BibTex]


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Hybrid IDM/Impedance learning in human movements

Burdet, E., Teng, K., Chew, C., Peters, J., , B.

In ISHF 2001, 1, pages: 1-9, 1st International Symposium on Measurement, Analysis and Modeling of Human Functions (ISHF2001), September 2001 (inproceedings)

Abstract
In spite of motor output variability and the delay in the sensori-motor, humans routinely perform intrinsically un- stable tasks. The hybrid IDM/impedance learning con- troller presented in this paper enables skilful performance in strong stable and unstable environments. It consid- ers motor output variability identified from experimen- tal data, and contains two modules concurrently learning the endpoint force and impedance adapted to the envi- ronment. The simulations suggest how humans learn to skillfully perform intrinsically unstable tasks. Testable predictions are proposed.

PDF Web [BibTex]

PDF Web [BibTex]


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Calibration of Digital Amateur Cameras

Urbanek, M., Horaud, R., Sturm, P.

(RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]

Web [BibTex]


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Combining Off- and On-line Calibration of a Digital Camera

Urbanek, M., Horaud, R., Sturm, P.

In In Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, pages: 99-106, In Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, June 2001 (inproceedings)

Abstract
We introduce a novel outlook on the self­calibration task, by considering images taken by a camera in motion, allowing for zooming and focusing. Apart from the complex relationship between the lens control settings and the intrinsic camera parameters, a prior off­line calibration allows to neglect the setting of focus, and to fix the principal point and aspect ratio throughout distinct views. Thus, the calibration matrix is dependent only on the zoom position. Given a fully calibrated reference view, one has only one parameter to estimate for any other view of the same scene, in order to calibrate it and to be able to perform metric reconstructions. We provide a close­form solution, and validate the reliability of the algorithm with experiments on real images. An important advantage of our method is a reduced ­ to one ­ number of critical camera configurations, associated with it. Moreover, we propose a method for computing the epipolar geometry of two views, taken from different positions and with different (spatial) resolutions; the idea is to take an appropriate third view, that is "easy" to match with the other two.

ZIP [BibTex]

ZIP [BibTex]


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Centralization: A new method for the normalization of gene expression data

Zien, A., Aigner, T., Zimmer, R., Lengauer, T.

Bioinformatics, 17, pages: S323-S331, June 2001, Mathematical supplement available at http://citeseer.ist.psu.edu/574280.html (article)

Abstract
Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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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.

PDF [BibTex]

PDF [BibTex]


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Variationsverfahren zur Untersuchung von Grundzustandseigenschaften des Ein-Band Hubbard-Modells

Eichhorn, J.

Biologische Kybernetik, Technische Universität Dresden, Dresden/Germany, May 2001 (diplomathesis)

Abstract
Using different modifications of a new variational approach, statical groundstate properties of the one-band Hubbard model such as energy and staggered magnetisation are calculated. By taking into account additional fluctuations, the method ist gradually improved so that a very good description of the energy in one and two dimensions can be achieved. After a detailed discussion of the application in one dimension, extensions for two dimensions are introduced. By use of a modified version of the variational ansatz in particular a description of the quantum phase transition for the magnetisation should be possible.

PostScript [BibTex]

PostScript [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)

[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.

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.

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.

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.

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.

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.

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.

PDF Web [BibTex]

PDF Web [BibTex]


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Plaid maskers revisited: asymmetric plaids

Wichmann, F.

pages: 57, 4. T{\"u}binger Wahrnehmungskonferenz (TWK), March 2001 (poster)

Abstract
A large number of psychophysical and physiological experiments suggest that luminance patterns are independently analysed in channels responding to different bands of spatial frequency. There are, however, interactions among stimuli falling well outside the usual estimates of channels' bandwidths. Derrington & Henning (1989) first reported that, in 2-AFC sinusoidal-grating detection, plaid maskers, whose components are oriented symmetrically about the signal orientation, cause a substantially larger threshold elevation than would be predicted from their sinusoidal constituents alone. Wichmann & Tollin (1997a,b) and Wichmann & Henning (1998) confirmed and extended the original findings, measuring masking as a function of presentation time and plaid mask contrast. Here I investigate masking using plaid patterns whose components are asymmetrically positioned about the signal orientation. Standard temporal 2-AFC pattern discrimination experiments were conducted using plaid patterns and oblique sinusoidal gratings as maskers, and horizontally orientated sinusoidal gratings as signals. Signal and maskers were always interleaved on the display (refresh rate 152 Hz). As in the case of the symmetrical plaid maskers, substantial masking was observed for many of the asymmetrical plaids. Masking is neither a straightforward function of the plaid's constituent sinusoidal components nor of the periodicity of the luminance beats between components. These results cause problems for the notion that, even for simple stimuli, detection and discrimination are based on the outputs of channels tuned to limited ranges of spatial frequency and orientation, even if a limited set of nonlinear interactions between these channels is allowed.

Web [BibTex]

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.

[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.

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

DOI [BibTex]

DOI [BibTex]