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2006


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Learning an Interest Operator from Human Eye Movements

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M.

In CVPWR 2006, pages: page 24, (Editors: C Schmid and S Soatto and C Tomasi), IEEE Computer Society, Los Alamitos, CA, USA, 2006 Conference on Computer Vision and Pattern Recognition Workshop, April 2006 (inproceedings)

Abstract
We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine learning methods to infer an interest operator directly from eye movement data. That way, the operator provides a measure of biologically plausible interestingness. We describe the data collection, training, and evaluation process, and show that our learned saliency measure significantly accounts for human eye movements. Furthermore, we illustrate connections to existing interest operators, and present a multi-scale interest point detector based on the learned function.

PDF Web DOI [BibTex]

2006

PDF Web DOI [BibTex]


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Evaluating Predictive Uncertainty Challenge

Quinonero Candela, J., Rasmussen, C., Sinz, F., Bousquet, O., Schölkopf, B.

In Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, pages: 1-27, (Editors: J Quiñonero Candela and I Dagan and B Magnini and F d’Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop (MLCW), April 2006 (inproceedings)

Abstract
This Chapter presents the PASCAL Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provides a discussion with some lessons to be learnt. The Challenge was set up to evaluate the ability of Machine Learning algorithms to provide good “probabilistic predictions”, rather than just the usual “point predictions” with no measure of uncertainty, in regression and classification problems. Parti-cipants had to compete on a number of regression and classification tasks, and were evaluated by both traditional losses that only take into account point predictions and losses we proposed that evaluate the quality of the probabilistic predictions.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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The Effect of Artifacts on Dependence Measurement in fMRI

Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N.

Magnetic Resonance Imaging, 24(4):401-409, April 2006 (article)

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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An Automated Combination of Sequence Motif Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

(146), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006 (techreport)

Abstract
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. We propose an elegant and fully automated approach to building a prediction system for protein subcellular localization. We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We further propose a multiclass support vector machine method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we generalize our method to optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets.

PDF Web [BibTex]

PDF Web [BibTex]


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Einer für viele: Ein Linux-PC bedient mehrere Arbeitsplätze

Renner, M., Stark, S.

c‘t, 2006(10):228-235, April 2006 (article)

Abstract
Ein moderner PC ist rechenstark genug, um mehrere Anwender gleichzeitig zu bedienen; und Linux als Multi-User-System ist von Hause aus darauf vorbereitet, mehrere gleichzeitig angemeldete Benutzer mit einem eigenen grafischen Desktop zu versorgen. Mit einem Kernelpatch und ein wenig Bastelei lassen sich an einen Linux-PC sogar mehrere unabh{\"a}ngige Monitore, Tastaturen und M{\"a}use anschließen.

Web [BibTex]

Web [BibTex]


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Phase noise and the classification of natural images

Wichmann, F., Braun, D., Gegenfurtner, K.

Vision Research, 46(8-9):1520-1529, April 2006 (article)

Abstract
We measured the effect of global phase manipulations on a rapid animal categorization task. The Fourier spectra of our images of natural scenes were manipulated by adding zero-mean random phase noise at all spatial frequencies. The phase noise was the independent variable, uniformly and symmetrically distributed between 0 degree and ±180 degrees. Subjects were remarkably resistant to phase noise. Even with ±120 degree phase noise subjects were still performing at 75% correct. The high resistance of the subjects’ animal categorization rate to phase noise suggests that the visual system is highly robust to such random image changes. The proportion of correct answers closely followed the correlation between original and the phase noise-distorted images. Animal detection rate was higher when the same task was performed with contrast reduced versions of the same natural images, at contrasts where the contrast reduction mimicked that resulting from our phase randomization. Since the subjects’ categorization rate was better in the contrast experiment, reduction of local contrast alone cannot explain the performance in the phase noise experiment. This result obtained with natural images differs from those obtained for simple sinusoidal stimuli were performance changes due to phase changes are attributed to local contrast changes only. Thus the global phasechange accompanying disruption of image structure such as edges and object boundaries at different spatial scales reduces object classification over and above the performance deficit resulting from reducing contrast. Additional colour information improves the categorization performance by 2 %.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Training a Support Vector Machine in the Primal

Chapelle, O.

(147), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006, The version in the "Large Scale Kernel Machines" book is more up to date. (techreport)

Abstract
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and there is no reason for ignoring it. Moreover, from the primal point of view, new families of algorithms for large scale SVM training can be investigated.

PDF [BibTex]

PDF [BibTex]


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Kernel PCA for Image Compression

Huhle, B.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants

Danziger, S., Swamidass, S., Zeng, J., Dearth, L., Lu, Q., Cheng, J., Cheng, J., Hoang, V., Saigo, H., Luo, R., Baldi, P., Brachmann, R., Lathrop, R.

IEEE Transactions on Computational Biology and Bioinformatics, 3(2):114-125, April 2006 (article)

Abstract
Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three (24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early (overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R.H.L. for computation and R.K.B. for biology).

PDF DOI [BibTex]

PDF DOI [BibTex]


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A Direct Method for Building Sparse Kernel Learning Algorithms

Wu, M., Schölkopf, B., BakIr, G.

Journal of Machine Learning Research, 7, pages: 603-624, April 2006 (article)

Abstract
Many Kernel Learning Algorithms(KLA), including Support Vector Machine (SVM), result in a Kernel Machine (KM), such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build Sparse Kernel Learning Algorithms (SKLA) by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting KM is explicitly controlled while at the same time the performance of the resulting KM can be kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the SVM results in a concrete algorithm for building Sparse Large Margin Classifiers (SLMC). Further analysis of the SLMC algorithm indicates that it essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.

PDF PDF [BibTex]

PDF PDF [BibTex]


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An Inventory of Sequence Polymorphisms For Arabidopsis

Clark, R., Ossowski, S., Schweikert, G., Rätsch, G., Shinn, P., Zeller, G., Warthmann, N., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D.

17th International Conference on Arabidopsis Research, April 2006 (talk)

Abstract
We have used high-density oligonucleotide arrays to characterize common sequence variation in 20 wild strains of Arabidopsis thaliana that were chosen for maximal genetic diversity. Both strands of each possible SNP of the 119 Mb reference genome were represented on the arrays, which were hybridized with whole genome, isothermally amplified DNA to minimize ascertainment biases. Using two complementary approaches, a model based algorithm, and a newly developed machine learning method, we identified over 550,000 SNPs with a false discovery rate of ~ 0.03 (average of 1 SNP for every 216 bp of the genome). A heuristic algorithm predicted in addition ~700 highly polymorphic or deleted regions per accession. Over 700 predicted polymorphisms with major functional effects (e.g., premature stop codons, or deletions of coding sequence) were validated by dideoxy sequencing. Using this data set, we provide the first systematic description of the types of genes that harbor major effect polymorphisms in natural populations at moderate allele frequencies. The data also provide an unprecedented resource for the study of genetic variation in an experimentally tractable, multicellular model organism.

[BibTex]

[BibTex]


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Estimating Predictive Variances with Kernel Ridge Regression

Cawley, G., Talbot, N., Chapelle, O.

In MLCW 2005, pages: 56-77, (Editors: Quinonero-Candela, J. , I. Dagan, B. Magnini, F. D‘Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop, April 2006 (inproceedings)

Abstract
In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of training data. Both of them can be summarised in the predictive variance which can then be used to give confidence intervals. In this paper, we present various schemes for providing predictive variances for kernel ridge regression, especially in the case of a heteroscedastic regression, where the variance of the noise process contaminating the data is a smooth function of the explanatory variables. The use of leave-one-out cross-validation is shown to eliminate the bias inherent in estimates of the predictive variance. Results obtained on all three regression tasks comprising the predictive uncertainty challenge demonstrate the value of this approach.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Machine Learning and Applications in Biology

Shin, H.

6th Course in Bioinformatics for Molecular Biologist, March 2006 (talk)

Abstract
The emergence of the fields of computational biology and bioinformatics has alleviated the burden of solving many biological problems, saving the time and cost required for experiments and also providing predictions that guide new experiments. Within computational biology, machine learning algorithms have played a central role in dealing with the flood of biological data. The goal of this tutorial is to raise awareness and comprehension of machine learning so that biologists can properly match the task at hand to the corresponding analytical approach. We start by categorizing biological problem settings and introduce the general machine learning schemes that fit best to each or these categories. We then explore representative models in further detail, from traditional statistical models to recent kernel models, presenting several up-to-date research projects in bioinfomatics to exemplify how biological questions can benefit from a machine learning approach. Finally, we discuss how cooperation between biologists and machine learners might be made smoother.

PDF [BibTex]

PDF [BibTex]


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The Pedestal Effect is Caused by Off-Frequency Looking, not Nonlinear Transduction or Contrast Gain-Control

Wichmann, F., Henning, G.

9, pages: 174, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the ‘threshold’ in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5-octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

Web [BibTex]

Web [BibTex]


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Efficient tests for the deconvolution hypothesis

Langovoy, M.

Workshop on Statistical Inverse Problems, March 2006 (poster)

Web [BibTex]

Web [BibTex]


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Kernel extrapolation

Vishwanathan, SVN., Borgwardt, KM., Guttman, O., Smola, AJ.

Neurocomputing, 69(7-9):721-729, March 2006 (article)

Abstract
We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled method to combine many of these kernels and then extrapolate them. Central to our method is a theorem for matrix approximation, and an extension of the representer theorem to handle multiple joint regularization constraints. Experiments in protein classification demonstrate the feasibility of our approach.

Web DOI [BibTex]

Web DOI [BibTex]


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ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments

Zhang, K., Chan, L.

In Independent Component Analysis and Blind Signal Separation, pages: 311-318, (Editors: J P Rosca and D Erdogmus and J C Príncipe and S Haykin), Springer, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA), March 2006, Series: Lecture Notes in Computer Science, Vol. 3889 (inproceedings)

Web [BibTex]

Web [BibTex]


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Statistical Properties of Kernel Principal Component Analysis

Blanchard, G., Bousquet, O., Zwald, L.

Machine Learning, 66(2-3):259-294, March 2006 (article)

Abstract
We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local Rademacher averages, we provide data-dependent and tight bounds for their convergence towards eigenvalues of the corresponding kernel operator. We perform these computations in a functional analytic framework which allows to deal implicitly with reproducing kernel Hilbert spaces of infinite dimension. This can have applications to various kernel algorithms, such as Support Vector Machines (SVM). We focus on Kernel Principal Component Analysis (KPCA) and, using such techniques, we obtain sharp excess risk bounds for the reconstruction error. In these bounds, the dependence on the decay of the spectrum and on the closeness of successive eigenvalues is made explicit.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Network-based de-noising improves prediction from microarray data

Kato, T., Murata, Y., Miura, K., Asai, K., Horton, P., Tsuda, K., Fujibuchi, W.

BMC Bioinformatics, 7(Suppl. 1):S4-S4, March 2006 (article)

Abstract
Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson‘s correlation coefficient between the true and predicted respon se values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning

Kuss, M.

Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Classification of Natural Scenes: Critical Features Revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

9, pages: 92, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification [1]. We successfully used linear discriminant analysis to classify a set of 11.000 images into “animal” and “non-animal” images based on their individual amplitude spectra only [2]. We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images ("best animals", "best distractors" and "worst animals", "worst distractors"). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced [3]. Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

Web [BibTex]

Web [BibTex]


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Machine Learning Methods For Estimating Operator Equations

Steinke, F., Schölkopf, B.

In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), pages: 6, (Editors: B Ninness and H Hjalmarsson), Elsevier, Oxford, United Kingdom, 14th IFAC Symposium on System Identification (SYSID), March 2006 (inproceedings)

Abstract
We consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality between minimizing a regularization functional derived from an operator and kernel regression methods. Standard machine learning model selection algorithms can then be interpreted as a search of the equation best fitting given data points. For many kernels this operator induced equation is a linear differential equation. Thus, we link a continuous-time system identification task with common machine learning methods. The presented link opens up a wide variety of methods to be applied to this system identification problem. In a series of experiments we demonstrate an example algorithm working on non-uniformly spaced data, giving special focus to the problem of identifying one system from multiple data recordings.

PDF Web [BibTex]

PDF Web [BibTex]


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Data mining problems and solutions for response modeling in CRM

Cho, S., Shin, H., Yu, E., Ha, K., MacLachlan, D.

Entrue Journal of Information Technology, 5(1):55-64, March 2006 (article)

Abstract
We present three data mining problems that are often encountered in building a response model. They are robust modeling, variable selection and data selection. Respective algorithmic solutions are given. They are bagging based ensemble, genetic algorithm based wrapper approach and nearest neighbor-based data selection in that order. A real world data set from Direct Marketing Educational Foundation, or DMEF4, is used to show their effectiveness. Proposed methods were found to solve the problems in a practical way.

PDF [BibTex]

PDF [BibTex]


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Factorial Coding of Natural Images: How Effective are Linear Models in Removing Higher-Order Dependencies?

Bethge, M.

9, pages: 90, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform coding framework, where it can be characterized by the constraint of a triangular filter matrix. A randomly sampled whitening basis and the Haar wavelet are included into the comparison as well. The comparison of all these methods is carried out for different patch sizes, ranging from 2x2 to 16x16 pixels. In spite of large differences in the shape of the basis functions, we find only small differences in the multi-information between all decorrelation transforms (5% or less) for all patch sizes. Among the second-order methods, PCA is optimal for small patch sizes and predictive coding performs best for large patch sizes. The extra gain achieved with ICA is always less than 2%. In conclusion, the `edge filters‘ found with ICA lead only to a surprisingly small improvement in terms of its actual objective.

Web [BibTex]


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Implicit Volterra and Wiener Series for Higher-Order Image Analysis

Franz, M., Schölkopf, B.

In Advances in Data Analysis: Proceedings of the 30th Annual Conference of The Gesellschaft für Klassifikation, 30, pages: 1, March 2006 (inproceedings)

Abstract
The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an alternative approach in which multiplicative pixel interactions are described by a series of Wiener functionals. Since the functionals are estimated implicitly via polynomial kernels, the combinatorial explosion associated with the classical higher-order statistics is avoided. In addition, the kernel framework allows for estimating infinite series expansions and for the regularized estimation of the Wiener series. First results show that image structures such as lines or corners can be predicted correctly, and that pixel interactions up to the order of five play an important role in natural images.

PDF [BibTex]

PDF [BibTex]


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Model-based Design Analysis and Yield Optimization

Pfingsten, T., Herrmann, D., Rasmussen, C.

IEEE Transactions on Semiconductor Manufacturing, 19(4):475-486, February 2006 (article)

Abstract
Fluctuations are inherent to any fabrication process. Integrated circuits and micro-electro-mechanical systems are particularly affected by these variations, and due to high quality requirements the effect on the devices’ performance has to be understood quantitatively. In recent years it has become possible to model the performance of such complex systems on the basis of design specifications, and model-based Sensitivity Analysis has made its way into industrial engineering. We show how an efficient Bayesian approach, using a Gaussian process prior, can replace the commonly used brute-force Monte Carlo scheme, making it possible to apply the analysis to computationally costly models. We introduce a number of global, statistically justified sensitivity measures for design analysis and optimization. Two models of integrated systems serve us as case studies to introduce the analysis and to assess its convergence properties. We show that the Bayesian Monte Carlo scheme can save costly simulation runs and can ensure a reliable accuracy of the analysis.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Prenatal development of ocular dominance and orientation maps in a self-organizing model of V1

Jegelka, S., Bednar, J., Miikkulainen, R.

Neurocomputing, 69(10-12):1291-1296, February 2006 (article)

Abstract
How orientation and ocular-dominance (OD) maps develop before visual experience begins is controversial. Possible influences include molecular signals and spontaneous activity, but their contributions remain unclear. This paper presents LISSOM simulations suggesting that previsual spontaneous activity alone is sufficient for realistic OR and OD maps to develop. Individual maps develop robustly with various previsual patterns, and are aided by background noise. However, joint OR/OD maps depend crucially on how correlated the patterns are between eyes, even over brief initial periods. Therefore, future biological experiments should account for multiple activity sources, and should measure map interactions rather than maps of single features.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Weighting of experimental evidence in macromolecular structure determination

Habeck, M., Rieping, W., Nilges, M.

Proceedings of the National Academy of Sciences of the United States of America, 103(6):1756-1761, February 2006 (article)

Abstract
The determination of macromolecular structures requires weighting of experimental evidence relative to prior physical information. Although it can critically affect the quality of the calculated structures, experimental data are routinely weighted on an empirical basis. At present, cross-validation is the most rigorous method to determine the best weight. We describe a general method to adaptively weight experimental data in the course of structure calculation. It is further shown that the necessity to define weights for the data can be completely alleviated. We demonstrate the method on a structure calculation from NMR data and find that the resulting structures are optimal in terms of accuracy and structural quality. Our method is devoid of the bias imposed by an empirical choice of the weight and has some advantages over estimating the weight by cross-validation.

Web DOI [BibTex]

Web DOI [BibTex]


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Cross-Validation Optimization for Structured Hessian Kernel Methods

Seeger, M., Chapelle, O.

Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2006 (techreport)

Abstract
We address the problem of learning hyperparameters in kernel methods for which the Hessian of the objective is structured. We propose an approximation to the cross-validation log likelihood whose gradient can be computed analytically, solving the hyperparameter learning problem efficiently through nonlinear optimization. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels or to large datasets. When applied to the problem of multi-way classification, our method scales linearly in the number of classes and gives rise to state-of-the-art results on a remote imaging task.

PDF Web [BibTex]

PDF Web [BibTex]


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Subspace identification through blind source separation

Grosse-Wentrup, M., Buss, M.

IEEE Signal Processing Letters, 13(2):100-103, February 2006 (article)

Abstract
Given a linear and instantaneous mixture model, we prove that for blind source separation (BSS) algorithms based on mutual information, only sources with non-Gaussian distribution are consistently reconstructed independent of initial conditions. This allows the identification of non-Gaussian sources and consequently the identification of signal and noise subspaces through BSS. The results are illustrated with a simple example, and the implications for a variety of signal processing applications, such as denoising and model identification, are discussed.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Classification of Faces in Man and Machine

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

Neural Computation, 18(1):143-165, January 2006 (article)

PDF Web [BibTex]

PDF Web [BibTex]


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Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Web [BibTex]

Web [BibTex]


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Class prediction from time series gene expression profiles using dynamical systems kernels

Borgwardt, KM., Vishwanathan, SVN., Kriegel, H-P.

In pages: 547-558, (Editors: Altman, R.B. A.K. Dunker, L. Hunter, T. Murray, T.E. Klein), World Scientific, Singapore, Pacific Symposium on Biocomputing (PSB), January 2006 (inproceedings)

Abstract
We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal characteristics of the data. More specifically, we model the evolution of the gene expression profiles as a Linear Time Invariant (LTI) dynamical system and estimate its model parameters. A kernel on dynamical systems is then used to classify these time series. We successfully test our approach on a published dataset to predict response to drug therapy in Multiple Sclerosis patients. For pharmacogenomics, our method offers a huge potential for advanced computational tools in disease diagnosis, and disease and drug therapy outcome prognosis.

PDF Web [BibTex]

PDF Web [BibTex]


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Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

Sun, X., Janzing, D., Schölkopf, B.

In Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, pages: 1-11, ISAIM, January 2006 (inproceedings)

Abstract
We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this paper, we discuss our inference rule for causal relationships between two variables in detail, apply it to a real-world temperature data set with known causality and show that our method provides a correct result for the example.

PDF Web [BibTex]

PDF Web [BibTex]


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Dimension Reduction as a Deflation Method in ICA

Zhang, K., Chan, L.

IEEE Signal Processing Letters, 13(1):45-48, 2006 (article)

Web [BibTex]

Web [BibTex]


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Extensions of ICA for Causality Discovery in the Hong Kong Stock Market

Zhang, K., Chan, L.

In Neural Information Processing, 13th International Conference, ICONIP 2006, pages: 400-409, (Editors: I King and J Wang and L Chan and D L Wang), Springer, 13th International Conference on Neural Information Processing (ICONIP), 2006, Lecture Notes in Computer Science, 2006, Volume 4234/2006 (inproceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Enhancement of source independence for blind source separation

Zhang, K., Chan, L.

In Independent Component Analysis and Blind Signal Separation, LNCS 3889, pages: 731-738, (Editors: J. Rosca and D. Erdogmus and JC Príncipe und S. Haykin), Springer, Berlin, Germany, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA), 2006, Lecture Notes in Computer Science, 2006, Volume 3889/2006 (inproceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Semigroups applied to transport and queueing processes

Radl, A.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, 2006 (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Apparatus for Inspecting Alignment Film of Liquid Crystal Display and Method Thereof

Park, MW., Son, HI., Kim, SJ., Kim, KI., Yang, JW.

Max-Planck-Gesellschaft, Biologische Kybernetik, 2006 (patent)

[BibTex]

[BibTex]


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ICA with Sparse Connections

Zhang, K., Chan, L.

In Intelligent Data Engineering and Automated Learning – IDEAL 2006, pages: 530-537, (Editors: E Corchado and H Yin and V Botti und Colin Fyfe), Springer, 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), 2006, Lecture Notes in Computer Science, 2006, Volume 4224/2006 (inproceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Classification of natural scenes: critical features revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 251, 2006 (poster)

[BibTex]

[BibTex]


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Local Alignment Kernels for Protein Homology Detection

Saigo, H.

Biologische Kybernetik, Kyoto University, Kyoto, Japan, 2006 (phdthesis)

[BibTex]

[BibTex]


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Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment

Quinonero Candela, J., Dagan, I., Magnini, B., Lauria, F.

Proceedings of the First Pascal Machine Learning Challenges Workshop on Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment (MLCW 2005), pages: 462, Lecture Notes in Computer Science, Springer, Heidelberg, Germany, First Pascal Machine Learning Challenges Workshop (MLCW), 2006 (proceedings)

Abstract
This book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005. The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; the second challenge was to recognize objects from a number of visual object classes in realistic scenes; the third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.

Web DOI [BibTex]

Web DOI [BibTex]


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Texture and haptic cues in slant discrimination: combination is sensitive to reliability but not statistically optimal

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

Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen (TeaP 2006), 48, pages: 80, 2006 (poster)

[BibTex]

[BibTex]


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Symbol Recognition with Kernel Density Matching

Zhang, W., Wenyin, L., Zhang, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2020-2024, 2006 (article)

Abstract
We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.

Web [BibTex]

Web [BibTex]


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Combining a Filter Method with SVMs

Lal, T., Chapelle, O., Schölkopf, B.

In Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing, Vol. 207, pages: 439-446, Studies in Fuzziness and Soft Computing ; 207, (Editors: I Guyon and M Nikravesh and S Gunn and LA Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the training sets of the competition data. We choose the data set Arcene as an example to explain the approach step by step. In our view the point of this competition was the construction of a well performing classifier rather than the systematic analysis of a specific approach. This is why our search for the best classifier was only guided by the described methods and that we deviated from the road map at several occasions. All calculations were done with the software Spider [2004].

PDF DOI [BibTex]

PDF DOI [BibTex]


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Apparatus for Inspecting Flat Panel Display and Method Thereof

Yang, JW., Kim, KI., Son, HI.

Max-Planck-Gesellschaft, Biologische Kybernetik, 2006 (patent)

[BibTex]

[BibTex]


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An adaptive method for subband decomposition ICA

Zhang, K., Chan, L.

Neural Computation, 18(1):191-223, 2006 (article)

Abstract
Subband decomposition ICA (SDICA), an extension of ICA, assumes that each source is represented as the sum of some independent subcomponents and dependent subcomponents, which have different frequency bands. In this article, we first investigate the feasibility of separating the SDICA mixture in an adaptive manner. Second, we develop an adaptive method for SDICA, namely band-selective ICA (BS-ICA), which finds the mixing matrix and the estimate of the source independent subcomponents. This method is based on the minimization of the mutual information between outputs. Some practical issues are discussed. For better applicability, a scheme to avoid the high-dimensional score function difference is given. Third, we investigate one form of the overcomplete ICA problems with sources having specific frequency characteristics, which BS-ICA can also be used to solve. Experimental results illustrate the success of the proposed method for solving both SDICA and the over-complete ICA problems.

Web DOI [BibTex]

Web DOI [BibTex]