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2002


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Numerical evolution of axisymmetric, isolated systems in general relativity

Frauendiener, J., Hein, M.

Physical Review D, 66, pages: 124004-124004, 2002 (article)

Abstract
We describe in this article a new code for evolving axisymmetric isolated systems in general relativity. Such systems are described by asymptotically flat space-times, which have the property that they admit a conformal extension. We are working directly in the extended conformal manifold and solve numerically Friedrich's conformal field equations, which state that Einstein's equations hold in the physical space-time. Because of the compactness of the conformal space-time the entire space-time can be calculated on a finite numerical grid. We describe in detail the numerical scheme, especially the treatment of the axisymmetry and the boundary.

GZIP [BibTex]

2002

GZIP [BibTex]


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Marginalized kernels for biological sequences

Tsuda, K., Kin, T., Asai, K.

Bioinformatics, 18(Suppl 1):268-275, 2002 (article)

PDF [BibTex]

PDF [BibTex]


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Localized Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

In Proceedings of the 15th annual conference on Computational Learning Theory, pages: 44-58, Proceedings of the 15th annual conference on Computational Learning Theory, 2002 (inproceedings)

Abstract
We investigate the behaviour of global and local Rademacher averages. We present new error bounds which are based on the local averages and indicate how data-dependent local averages can be estimated without {it a priori} knowledge of the class at hand.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Film Cooling: A Comparative Study of Different Heaterfoil Configurations for Liquid Crystals Experiments

Vogel, G., Graf, ABA., Weigand, B.

In ASME TURBO EXPO 2002, Amsterdam, GT-2002-30552, ASME TURBO EXPO, Amsterdam, 2002 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Web [BibTex]


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Application of Monte Carlo Methods to Psychometric Function Fitting

Wichmann, F.

Proceedings of the 33rd European Conference on Mathematical Psychology, pages: 44, 2002 (poster)

Abstract
The psychometric function relates an observer's performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. Here I describe methods to (1) fitting psychometric functions, (2) assessing goodness-of-fit, and (3) providing confidence intervals for the function's parameters and other estimates derived from them. First I describe a constrained maximum-likelihood method for parameter estimation. Using Monte-Carlo simulations I demonstrate that it is important to have a fitting method that takes stimulus-independent errors (or "lapses") into account. Second, a number of goodness-of-fit tests are introduced. Because psychophysical data sets are usually rather small I advocate the use of Monte Carlo resampling techniques that do not rely on asymptotic theory for goodness-of-fit assessment. Third, a parametric bootstrap is employed to estimate the variability of fitted parameters and derived quantities such as thresholds and slopes. I describe how the bootstrap bridging assumption, on which the validity of the procedure depends, can be tested without incurring too high a cost in computation time. Finally I describe how the methods can be extended to test hypotheses concerning the form and shape of several psychometric functions. Software describing the methods is available (http://www.bootstrap-software.com/psignifit/), as well as articles describing the methods in detail (Wichmann&Hill, Perception&Psychophysics, 2001a,b).

[BibTex]

[BibTex]


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Stability and Generalization

Bousquet, O., Elisseeff, A.

Journal of Machine Learning Research, 2, pages: 499-526, 2002 (article)

Abstract
We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Subspace information criterion for non-quadratic regularizers – model selection for sparse regressors

Tsuda, K., Sugiyama, M., Müller, K.

IEEE Trans Neural Networks, 13(1):70-80, 2002 (article)

PDF [BibTex]

PDF [BibTex]


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Modeling splicing sites with pairwise correlations

Arita, M., Tsuda, K., Asai, K.

Bioinformatics, 18(Suppl 2):27-34, 2002 (article)

PDF [BibTex]

PDF [BibTex]


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Observations on the Nyström Method for Gaussian Process Prediction

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

Abstract
A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nystr{\"o}m method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nystr{\"o}m method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nystr{\"o}m approximation would be expected to provide a good approximation to exact GP regression.

PostScript [BibTex]

PostScript [BibTex]


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Perfusion Quantification using Gaussian Process Deconvolution

Andersen, IK., Szymkowiak, A., Rasmussen, CE., Hanson, LG., Marstrand, JR., Larsson, HBW., Hansen, LK.

Magnetic Resonance in Medicine, (48):351-361, 2002 (article)

Abstract
The quantification of perfusion using dynamic susceptibility contrast MR imaging requires deconvolution to obtain the residual impulse-response function (IRF). Here, a method using a Gaussian process for deconvolution, GPD, is proposed. The fact that the IRF is smooth is incorporated as a constraint in the method. The GPD method, which automatically estimates the noise level in each voxel, has the advantage that model parameters are optimized automatically. The GPD is compared to singular value decomposition (SVD) using a common threshold for the singular values and to SVD using a threshold optimized according to the noise level in each voxel. The comparison is carried out using artificial data as well as using data from healthy volunteers. It is shown that GPD is comparable to SVD variable optimized threshold when determining the maximum of the IRF, which is directly related to the perfusion. GPD provides a better estimate of the entire IRF. As the signal to noise ratio increases or the time resolution of the measurements increases, GPD is shown to be superior to SVD. This is also found for large distribution volumes.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Tracking a Small Set of Experts by Mixing Past Posteriors

Bousquet, O., Warmuth, M.

Journal of Machine Learning Research, 3, pages: 363-396, (Editors: Long, P.), 2002 (article)

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

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A femoral arteriovenous shunt facilitates arterial whole blood sampling in animals

Weber, B., Burger, C., Biro, P., Buck, A.

Eur J Nucl Med Mol Imaging, 29, pages: 319-323, 2002 (article)

[BibTex]

[BibTex]


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Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

Bousquet, O., Koltchinskii, V., Panchenko, D.

In Proceedings of the 15th annual conference on Computational Learning Theory, Proceedings of the 15th annual conference on Computational Learning Theory, 2002 (inproceedings)

Abstract
We investigate measures of complexity of function classes based on continuity moduli of Gaussian and Rademacher processes. For Gaussian processes, we obtain bounds on the continuity modulus on the convex hull of a function class in terms of the same quantity for the class itself. We also obtain new bounds on generalization error in terms of localized Rademacher complexities. This allows us to prove new results about generalization performance for convex hulls in terms of characteristics of the base class. As a byproduct, we obtain a simple proof of some of the known bounds on the entropy of convex hulls.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Contrast discrimination with pulse-trains in pink noise

Henning, G., Bird, C., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

Abstract
Detection performance was measured with sinusoidal and pulse-train gratings. Although the 2.09-c/deg pulse-train, or line gratings, contained at least 8 harmonics all at equal contrast, they were no more detectable than their most detectable component. The addition of broadband pink noise designed to equalize the detectability of the components of the pulse train made the pulse train about a factor of four more detectable than any of its components. However, in contrast-discrimination experiments, with a pedestal or masking grating of the same form and phase as the signal and 15% contrast, the noise did not affect the discrimination performance of the pulse train relative to that obtained with its sinusoidal components. We discuss the implications of these observations for models of early vision in particular the implications for possible sources of internal noise.

PDF [BibTex]

PDF [BibTex]


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A kernel approach for learning from almost orthogonal patterns

Schölkopf, B., Weston, J., Eskin, E., Leslie, C., Noble, W.

In Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, 2430/2431, pages: 511-528, Lecture Notes in Computer Science, (Editors: T Elomaa and H Mannila and H Toivonen), Springer, Berlin, Germany, 13th European Conference on Machine Learning (ECML) and 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2002), 2002 (inproceedings)

PostScript DOI [BibTex]

PostScript DOI [BibTex]


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Optimal linear estimation of self-motion - a real-world test of a model of fly tangential neurons

Franz, MO.

SAB 02 Workshop, Robotics as theoretical biology, 7th meeting of the International Society for Simulation of Adaptive Behaviour (SAB), (Editors: Prescott, T.; Webb, B.), 2002 (poster)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion (see example in Fig.1). We examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal linear estimator incorporating prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The optimal estimator is tested on a gantry carrying an omnidirectional vision sensor that can be moved along three translational and one rotational degree of freedom. The experiments indicate that the proposed approach yields accurate results for rotation estimates, independently of the current translation and scene layout. Translation estimates, however, turned out to be sensitive to simultaneous rotation and to the particular distance distribution of the scene. The gantry experiments confirm that the receptive field organization of the tangential neurons allows them, as an ensemble, to extract self-motion from the optic flow.

PDF [BibTex]

PDF [BibTex]


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Choosing Multiple Parameters for Support Vector Machines

Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.

Machine Learning, 46(1):131-159, 2002 (article)

Abstract
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVM) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Infinite Mixtures of Gaussian Process Experts

Rasmussen, CE., Ghahramani, Z.

In (Editors: Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin), 2002 (inproceedings)

Abstract
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Marginalized kernels for RNA sequence data analysis

Kin, T., Tsuda, K., Asai, K.

In Genome Informatics 2002, pages: 112-122, (Editors: Lathtop, R. H.; Nakai, K.; Miyano, S.; Takagi, T.; Kanehisa, M.), Genome Informatics, 2002, (Best Paper Award) (inproceedings)

Web [BibTex]

Web [BibTex]


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Luminance Artifacts on CRT Displays

Wichmann, F.

In IEEE Visualization, pages: 571-574, (Editors: Moorhead, R.; Gross, M.; Joy, K. I.), IEEE Visualization, 2002 (inproceedings)

Abstract
Most visualization panels today are still built around cathode-ray tubes (CRTs), certainly on personal desktops at work and at home. Whilst capable of producing pleasing images for common applications ranging from email writing to TV and DVD presentation, it is as well to note that there are a number of nonlinear transformations between input (voltage) and output (luminance) which distort the digital and/or analogue images send to a CRT. Some of them are input-independent and hence easy to fix, e.g. gamma correction, but others, such as pixel interactions, depend on the content of the input stimulus and are thus harder to compensate for. CRT-induced image distortions cause problems not only in basic vision research but also for applications where image fidelity is critical, most notably in medicine (digitization of X-ray images for diagnostic purposes) and in forms of online commerce, such as the online sale of images, where the image must be reproduced on some output device which will not have the same transfer function as the customer's CRT. I will present measurements from a number of CRTs and illustrate how some of their shortcomings may be problematic for the aforementioned applications.

[BibTex]

[BibTex]

2000


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Knowledge Discovery in Databases: An Information Retrieval Perspective

Ong, CS.

Malaysian Journal of Computer Science, 13(2):54-63, December 2000 (article)

Abstract
The current trend of increasing capabilities in data generation and collection has resulted in an urgent need for data mining applications, also called knowledge discovery in databases. This paper identifies and examines the issues involved in extracting useful grains of knowledge from large amounts of data. It describes a framework to categorise data mining systems. The author also gives an overview of the issues pertaining to data pre processing, as well as various information gathering methodologies and techniques. The paper covers some popular tools such as classification, clustering, and generalisation. A summary of statistical and machine learning techniques used currently is also provided.

PDF [BibTex]

2000

PDF [BibTex]


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A real-time model of the human knee for application in virtual orthopaedic trainer

Peters, J., Riener, R.

In Proceedings of the 10th International Conference on BioMedical Engineering (ICBME 2000), 10, pages: 1-2, 10th International Conference on BioMedical Engineering (ICBME) , December 2000 (inproceedings)

Abstract
In this paper a real-time capable computational model of the human knee is presented. The model describes the passive elastic joint characteristics in six degrees-of-freedom (DOF). A black-box approach was chosen, where experimental data were approximated by piecewise polynomial functions. The knee model has been applied in a the Virtual Orthopaedic Trainer, which can support training of physical knee evaluation required for diagnosis and surgical planning.

PDF Web [BibTex]

PDF Web [BibTex]


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A Simple Iterative Approach to Parameter Optimization

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

Journal of Computational Biology, 7(3,4):483-501, November 2000 (article)

Abstract
Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a scoring function combines the values for different parameters of possible sequence-to-structure alignments into a single score to allow for unambiguous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, a partial ordering on optimal alignments to other structures, e.g., derived from structural comparisons, may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a heuristic approach: iterating the computation of solutions (here, threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via systematic calibration methods. For our application (i.e., threading), this iterative approach results in structurally meaningful weights that significantly improve performance on both the training and the test data sets. In addition, the optimized parameters show significant improvements on the recognition rate for a grossly enlarged comprehensive benchmark, a modified recognition protocol as well as modified alignment types (local instead of global and profiles instead of single sequences). These results show the general validity of the optimized weights for the given threading program and the associated scoring contributions.

Web [BibTex]

Web [BibTex]


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Identification of Drug Target Proteins

Zien, A., Küffner, R., Mevissen, T., Zimmer, R., Lengauer, T.

ERCIM News, 43, pages: 16-17, October 2000 (article)

Web [BibTex]

Web [BibTex]


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On Designing an Automated Malaysian Stemmer for the Malay Language

Tai, SY., Ong, CS., Abullah, NA.

In Fifth International Workshop on Information Retrieval with Asian Languages, pages: 207-208, ACM Press, New York, NY, USA, Fifth International Workshop on Information Retrieval with Asian Languages, October 2000 (inproceedings)

Abstract
Online and interactive information retrieval systems are likely to play an increasing role in the Malay Language community. To facilitate and automate the process of matching morphological term variants, a stemmer focusing on common affix removal algorithms is proposed as part of the design of an information retrieval system for the Malay Language. Stemming is a morphological process of normalizing word tokens down to their essential roots. The proposed stemmer strips prefixes and suffixes off the word. The experiment conducted with web sites selected from the World Wide Web has exhibited substantial improvements in the number of words indexed.

PostScript Web DOI [BibTex]

PostScript Web DOI [BibTex]


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Robust ensemble learning

Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.

In Advances in Large Margin Classifiers, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

[BibTex]

[BibTex]


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Entropy numbers for convex combinations and MLPs

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

In Advances in Large Margin Classifiers, pages: 369-387, Neural Information Processing Series, (Editors: AJ Smola and PL Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA,, October 2000 (inbook)

[BibTex]

[BibTex]


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Ensemble of Specialized Networks based on Input Space Partition

Shin, H., Lee, H., Cho, S.

In Proc. of the Korean Operations Research and Management Science Conference, pages: 33-36, Korean Operations Research and Management Science Conference, October 2000 (inproceedings)

[BibTex]

[BibTex]


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DES Approach Failure Recovery of Pump-valve System

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

In Korean Society of Precision Engineering (KSPE) Conference, pages: 647-650, Annual Meeting of the Korean Society of Precision Engineering (KSPE), October 2000 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Natural Regularization from Generative Models

Oliver, N., Schölkopf, B., Smola, A.

In Advances in Large Margin Classifiers, pages: 51-60, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

[BibTex]

[BibTex]


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Advances in Large Margin Classifiers

Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D.

pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)

Abstract
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

Web [BibTex]

Web [BibTex]


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Ensemble Learning Algorithm of Specialized Networks

Shin, H., Lee, H., Cho, S.

In Proc. of the Korea Information Science Conference, pages: 308-310, Korea Information Science Conference, October 2000 (inproceedings)

[BibTex]

[BibTex]


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DES Approach Failure Diagnosis of Pump-valve System

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

In Korean Society of Precision Engineering (KSPE) Conference, pages: 643-646, Annual Meeting of the Korean Society of Precision Engineering (KSPE), October 2000 (inproceedings)

Abstract
As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.

PDF [BibTex]

PDF [BibTex]


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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.

Bioinformatics, 16(9):799-807, September 2000 (article)

Abstract
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

Web DOI [BibTex]

Web DOI [BibTex]


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Three-dimensional reconstruction of planar scenes

Urbanek, M.

Biologische Kybernetik, INP Grenoble, Warsaw University of Technology, September 2000 (diplomathesis)

Abstract
For a planar scene, we propose an algorithm to estimate its 3D structure. Homographies between corresponding planes are employed in order to recover camera motion parameters - between camera positions from which images of the scene were taken. Cases of one- and multiple- corresponding planes present on the scene are distinguished. Solutions are proposed for both cases.

ZIP [BibTex]

ZIP [BibTex]


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Analysis of Gene Expression Data with Pathway Scores

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

In ISMB 2000, pages: 407-417, AAAI Press, Menlo Park, CA, USA, 8th International Conference on Intelligent Systems for Molecular Biology, August 2000 (inproceedings)

Abstract
We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for different problem specifications. The significance of the scores for the investigated pathways is assessed by comparison to a number of scores for random pathways. We show that simple scoring functions can assign statistically significant scores to biologically relevant pathways. This suggests that the combination of appropriate scoring functions with the systematic generation of pathways can be used in order to select the most interesting pathways based on gene expression measurements.

PDF [BibTex]

PDF [BibTex]


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A Meanfield Approach to the Thermodynamics of a Protein-Solvent System with Application to the Oligomerization of the Tumour Suppressor p53.

Noolandi, J., Davison, TS., Vokel, A., Nie, F., Kay, C., Arrowsmith, C.

Proceedings of the National Academy of Sciences of the United States of America, 97(18):9955-9960, August 2000 (article)

Web [BibTex]

Web [BibTex]


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Observational Learning with Modular Networks

Shin, H., Lee, H., Cho, S.

In Lecture Notes in Computer Science (LNCS 1983), LNCS 1983, pages: 126-132, Springer-Verlag, Heidelberg, International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), July 2000 (inproceedings)

Abstract
Observational learning algorithm is an ensemble algorithm where each network is initially trained with a bootstrapped data set and virtual data are generated from the ensemble for training. Here we propose a modular OLA approach where the original training set is partitioned into clusters and then each network is instead trained with one of the clusters. Networks are combined with different weighting factors now that are inversely proportional to the distance from the input vector to the cluster centers. Comparison with bagging and boosting shows that the proposed approach reduces generalization error with a smaller number of networks employed.

PDF [BibTex]

PDF [BibTex]


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The Infinite Gaussian Mixture Model

Rasmussen, CE.

In Advances in Neural Information Processing Systems 12, pages: 554-560, (Editors: Solla, S.A. , T.K. Leen, K-R Müller), MIT Press, Cambridge, MA, USA, Thirteenth Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the ``right'' number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.

PDF Web [BibTex]

PDF Web [BibTex]


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Generalization Abilities of Ensemble Learning Algorithms

Shin, H., Jang, M., Cho, S.

In Proc. of the Korean Brain Society Conference, pages: 129-133, Korean Brain Society Conference, June 2000 (inproceedings)

[BibTex]

[BibTex]


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Support vector method for novelty detection

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

In Advances in Neural Information Processing Systems 12, pages: 582-588, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
Suppose you are given some dataset drawn from an underlying probability distribution ¤ and you want to estimate a “simple” subset ¥ of input space such that the probability that a test point drawn from ¤ lies outside of ¥ equals some a priori specified ¦ between § and ¨. We propose a method to approach this problem by trying to estimate a function © which is positive on ¥ and negative on the complement. The functional form of © is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.

PDF Web [BibTex]

PDF Web [BibTex]


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Solving Satisfiability Problems with Genetic Algorithms

Harmeling, S.

In Genetic Algorithms and Genetic Programming at Stanford 2000, pages: 206-213, (Editors: Koza, J. R.), Stanford Bookstore, Stanford, CA, USA, June 2000 (inbook)

Abstract
We show how to solve hard 3-SAT problems using genetic algorithms. Furthermore, we explore other genetic operators that may be useful to tackle 3-SAT problems, and discuss their pros and cons.

PDF [BibTex]

PDF [BibTex]


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v-Arc: Ensemble Learning in the Presence of Outliers

Rätsch, G., Schölkopf, B., Smola, A., Müller, K., Onoda, T., Mika, S.

In Advances in Neural Information Processing Systems 12, pages: 561-567, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
AdaBoost and other ensemble methods have successfully been applied to a number of classification tasks, seemingly defying problems of overfitting. AdaBoost performs gradient descent in an error function with respect to the margin, asymptotically concentrating on the patterns which are hardest to learn. For very noisy problems, however, this can be disadvantageous. Indeed, theoretical analysis has shown that the margin distribution, as opposed to just the minimal margin, plays a crucial role in understanding this phenomenon. Loosely speaking, some outliers should be tolerated if this has the benefit of substantially increasing the margin on the remaining points. We propose a new boosting algorithm which allows for the possibility of a pre-specified fraction of points to lie in the margin area or even on the wrong side of the decision boundary.

PDF Web [BibTex]

PDF Web [BibTex]


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Invariant feature extraction and classification in kernel spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

In Advances in neural information processing systems 12, pages: 526-532, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Transductive Inference for Estimating Values of Functions

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

In Advances in Neural Information Processing Systems 12, pages: 421-427, (Editors: Solla, S.A. , T.K. Leen, K-R Müller), MIT Press, Cambridge, MA, USA, Thirteenth Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
We introduce an algorithm for estimating the values of a function at a set of test points $x_1^*,dots,x^*_m$ given a set of training points $(x_1,y_1),dots,(x_ell,y_ell)$ without estimating (as an intermediate step) the regression function. We demonstrate that this direct (transductive) way for estimating values of the regression (or classification in pattern recognition) is more accurate than the traditional one based on two steps, first estimating the function and then calculating the values of this function at the points of interest.

PDF Web [BibTex]

PDF Web [BibTex]


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The entropy regularization information criterion

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

In Advances in Neural Information Processing Systems 12, pages: 342-348, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
Effective methods of capacity control via uniform convergence bounds for function expansions have been largely limited to Support Vector machines, where good bounds are obtainable by the entropy number approach. We extend these methods to systems with expansions in terms of arbitrary (parametrized) basis functions and a wide range of regularization methods covering the whole range of general linear additive models. This is achieved by a data dependent analysis of the eigenvalues of the corresponding design matrix.

PDF Web [BibTex]

PDF Web [BibTex]


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Model Selection for Support Vector Machines

Chapelle, O., Vapnik, V.

In Advances in Neural Information Processing Systems 12, pages: 230-236, (Editors: Solla, S.A. , T.K. Leen, K-R Müller), MIT Press, Cambridge, MA, USA, Thirteenth Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that using these functionals, one can both predict the best choice of parameters of the model and the relative quality of performance for any value of parameter.

PDF Web [BibTex]

PDF Web [BibTex]