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2011


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PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

Seldin, Y., Laviolette, F., Shawe-Taylor, J., Peters, J., Auer, P.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2011 (techreport)

Abstract
We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This lemma provides an alternative tool to Hoeffding-Azuma inequality to bound concentration of martingale values. Our second approach is based on integration of Hoeffding-Azuma inequality with PAC-Bayesian analysis. We also introduce a way to apply PAC-Bayesian analysis in situation of limited feedback. We combine the new tools to derive PAC-Bayesian generalization and regret bounds for the multiarmed bandit problem. Although our regret bound is not yet as tight as state-of-the-art regret bounds based on other well-established techniques, our results significantly expand the range of potential applications of PAC-Bayesian analysis and introduce a new analysis tool to reinforcement learning and many other fields, where martingales and limited feedback are encountered.

ei

PDF Web [BibTex]

2011


PDF Web [BibTex]


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Non-stationary Correction of Optical Aberrations

Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.

(1), Max Planck Institute for Intelligent Systems, Tübingen, Germany, May 2011 (techreport)

Abstract
Taking a sharp photo at several megapixel resolution traditionally relies on high grade lenses. In this paper, we present an approach to alleviate image degradations caused by imperfect optics. We rely on a calibration step to encode the optical aberrations in a space-variant point spread function and obtain a corrected image by non-stationary deconvolution. By including the Bayer array in our image formation model, we can perform demosaicing as part of the deconvolution.

ei

PDF [BibTex]

PDF [BibTex]


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Crowdsourcing for optimisation of deconvolution methods via an iPhone application

Lang, A.

Hochschule Reutlingen, Germany, April 2011 (mastersthesis)

ei

[BibTex]

[BibTex]


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Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Nickisch, H., Seeger, M.

Max Planck Institute for Biological Cybernetics, March 2011 (techreport)

Abstract
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the marginal likelihood and contains many regularised risk approaches as special cases. Furthermore, we derive an efficient and provably convergent optimisation algorithm.

ei

Web [BibTex]

Web [BibTex]


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Multiple testing, uncertainty and realistic pictures

Langovoy, M., Wittich, O.

(2011-004), EURANDOM, Technische Universiteit Eindhoven, January 2011 (techreport)

Abstract
We study statistical detection of grayscale objects in noisy images. The object of interest is of unknown shape and has an unknown intensity, that can be varying over the object and can be negative. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required. We propose an algorithm that can be used to detect grayscale objects of unknown shapes in the presence of nonparametric noise of unknown level. Our algorithm is based on a nonparametric multiple testing procedure. We establish the limit of applicability of our method via an explicit, closed-form, non-asymptotic and nonparametric consistency bound. This bound is valid for a wide class of nonparametric noise distributions. We achieve this by proving an uncertainty principle for percolation on nite lattices.

ei

PDF [BibTex]

PDF [BibTex]


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Nonconvex proximal splitting: batch and incremental algorithms

Sra, S.

(2), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2011 (techreport)

Abstract
Within the unmanageably large class of nonconvex optimization, we consider the rich subclass of nonsmooth problems having composite objectives (this includes the extensively studied convex, composite objective problems as a special case). For this subclass, we introduce a powerful, new framework that permits asymptotically non-vanishing perturbations. In particular, we develop perturbation-based batch and incremental (online like) nonconvex proximal splitting algorithms. To our knowledge, this is the rst time that such perturbation-based nonconvex splitting algorithms are being proposed and analyzed. While the main contribution of the paper is the theoretical framework, we complement our results by presenting some empirical results on matrix factorization.

ei

PDF [BibTex]

PDF [BibTex]


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Model Learning in Robot Control

Nguyen-Tuong, D.

Albert-Ludwigs-Universität Freiburg, Germany, 2011 (phdthesis)

ei

[BibTex]

[BibTex]


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Iterative path integral stochastic optimal control: Theory and applications to motor control

Theodorou, E. A.

University of Southern California, University of Southern California, Los Angeles, CA, 2011 (phdthesis)

am

PDF [BibTex]

PDF [BibTex]


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Learning of grasp selection based on shape-templates

Herzog, A.

Karlsruhe Institute of Technology, 2011 (mastersthesis)

am

[BibTex]

[BibTex]


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Ferromagnetism of ZnO influenced by physical and chemical treatment

Chen, Y.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]


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Herstellung und Charakterisierung von ultradünnen, funktionellen CoFeB Filmen

Streckenbach, F.

Hochschule Esslingen / Hochschule Aalen, Esslingen / Aalen, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]


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Hydrogen adsorption on metal-organic frameworks

Streppel, B.

Universität Stuttgart, Stuttgart, 2011 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Piezo driven strain effects on magneto-crystalline anisotropy

Badr, E.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]


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Magnetooptische Untersuchungen an granularen und beschichteten MgB2 Filmen

Stahl, C.

Universität Stuttgart, Stuttgart, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]


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Mikromagnetismus der Wechselwirkung von Spinwellen mit Domänenwänden in Ferromagneten

Macke, S.

Universität Stuttgart, Stuttgart, 2011 (phdthesis)

mms

[BibTex]

[BibTex]


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Herstellung und Qualifizierung gesputterter Magnesiumdiboridschichten

Breyer, F.

Hochschule Aalen, Aalen, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]


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Study of krypton/xenon storage and separation in microporous frameworks

Soleimani Dorcheh, A.

Universität Darmstadt, Darmstadt, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]

2009


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Learning an Interactive Segmentation System

Nickisch, H., Kohli, P., Rother, C.

Max Planck Institute for Biological Cybernetics, December 2009 (techreport)

Abstract
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

ei

Web [BibTex]

2009


Web [BibTex]


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An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction

Harmeling, S., Sra, S., Hirsch, M., Schölkopf, B.

(187), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2009 (techreport)

Abstract
We develop an incremental generalized expectation maximization (GEM) framework to model the multiframe blind deconvolution problem. A simplistic version of this problem was recently studied by Harmeling etal~cite{harmeling09}. We solve a more realistic version of this problem which includes the following major features: (i) super-resolution ability emph{despite} noise and unknown blurring; (ii) saturation-correction, i.e., handling of overexposed pixels that can otherwise confound the image processing; and (iii) simultaneous handling of color channels. These features are seamlessly integrated into our incremental GEM framework to yield simple but efficient multiframe blind deconvolution algorithms. We present technical details concerning critical steps of our algorithms, especially to highlight how all operations can be written using matrix-vector multiplications. We apply our algorithm to real-world images from astronomy and super resolution tasks. Our experimental results show that our methods yield improve d resolution and deconvolution at the same time.

ei

PDF [BibTex]

PDF [BibTex]


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Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution

Hirsch, M., Sra, S., Schölkopf, B., Harmeling, S.

(188), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2009 (techreport)

Abstract
Ultimately being motivated by facilitating space-variant blind deconvolution, we present a class of linear transformations, that are expressive enough for space-variant filters, but at the same time especially designed for efficient matrix-vector-multiplications. Successful results on astronomical imaging through atmospheric turbulences and on noisy magnetic resonance images of constantly moving objects demonstrate the practical significance of our approach.

ei

PDF [BibTex]

PDF [BibTex]


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Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract
This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

ei

PDF [BibTex]

PDF [BibTex]


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Consistent Nonparametric Tests of Independence

Gretton, A., Györfi, L.

(172), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2009 (techreport)

Abstract
Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-likelihood) are defined when the empirical distribution of the variables is restricted to finite partitions. A third test statistic is defined as a kernel-based independence measure. Two kinds of tests are provided. Distribution-free strong consistent tests are derived on the basis of large deviation bounds on the test statistcs: these tests make almost surely no Type I or Type II error after a random sample size. Asymptotically alpha-level tests are obtained from the limiting distribution of the test statistics. For the latter tests, the Type I error converges to a fixed non-zero value alpha, and the Type II error drops to zero, for increasing sample size. All tests reject the null hypothesis of independence if the test statistics become large. The performance of the tests is evaluated experimentally on benchmark data.

ei

PDF [BibTex]

PDF [BibTex]


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Semi-supervised subspace analysis of human functional magnetic resonance imaging data

Shelton, J., Blaschko, M., Bartels, A.

(185), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2009 (techreport)

Abstract
Kernel Canonical Correlation Analysis is a very general technique for subspace learning that incorporates PCA and LDA as special cases. Functional magnetic resonance imaging (fMRI) acquired data is naturally amenable to these techniques as data are well aligned. fMRI data of the human brain is a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single- and multi-variate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of KCCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.

ei

PDF [BibTex]

PDF [BibTex]


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Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery

Blaschko, MB.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Motor Control and Learning in Table Tennis

Mülling, K.

Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

ei

[BibTex]

[BibTex]


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Hierarchical Clustering and Density Estimation Based on k-nearest-neighbor graphs

Drewe, P.

Eberhard Karls Universität Tübingen, Germany, 2009 (diplomathesis)

ei

[BibTex]

[BibTex]


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Learning with Structured Data: Applications to Computer Vision

Nowozin, S.

Technische Universität Berlin, Germany, 2009 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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From Differential Equations to Differential Geometry: Aspects of Regularisation in Machine Learning

Steinke, F.

Universität des Saarlandes, Saarbrücken, Germany, 2009 (phdthesis)

ei

PDF [BibTex]


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Magnetische L10-FePt Nanostrukturen für höchste Datenspeicherdichten

Breitling, A.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

[BibTex]

[BibTex]


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Ab-initio Elliott-Yafet modeling of ultrafast demagnetization after laser irradiation

Illg, C.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]


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Element specific investigation of the magnetization profile at the CrO2/RuO2 interface

Zafar, K.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]


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Bayesian Methods for Autonomous Learning Systems (Phd Thesis)

Ting, J.

Department of Computer Science, University of Southern California, Los Angeles, CA, 2009, clmc (phdthesis)

am

PDF [BibTex]

PDF [BibTex]


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The SL simulation and real-time control software package

Schaal, S.

University of Southern California, Los Angeles, CA, 2009, clmc (techreport)

Abstract
SL was originally developed as a Simulation Laboratory software package to allow creating complex rigid-body dynamics simulations with minimal development times. It was meant to complement a real-time robotics setup such that robot programs could first be debugged in simulation before trying them on the actual robot. For this purpose, the motor control setup of SL was copied from our experience with real-time robot setups with vxWorks (Windriver Systems, Inc.)Ñindeed, more than 90% of the code is identical to the actual robot software, as will be explained later in detail. As a result, SL is divided into three software components: 1) the generic code that is shared by the actual robot and the simulation, 2) the robot specific code, and 3) the simulation specific code. The robot specific code is tailored to the robotic environments that we have experienced over the years, in particular towards VME-based multi-processor real-time operating systems. The simulation specific code has all the components for OpenGL graphics simulations and mimics the robot multi-processor environment in simple C-code. Importantly, SL can be used stand-alone for creating graphics an-imationsÑthe heritage from real-time robotics does not restrict the complexity of possible simulations. This technical report describes SL in detail and can serve as a manual for new users of SL.

am

link (url) [BibTex]

link (url) [BibTex]


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Magnetic resonant reflectometry on exchange bias systems

Brück, S.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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The SL simulation and real-time control software package

Schaal, S.

University of Southern California, Los Angeles, CA, 2009, clmc (techreport)

Abstract
SL was originally developed as a Simulation Laboratory software package to allow creating complex rigid-body dynamics simulations with minimal development times. It was meant to complement a real-time robotics setup such that robot programs could first be debugged in simulation before trying them on the actual robot. For this purpose, the motor control setup of SL was copied from our experience with real-time robot setups with vxWorks (Windriver Systems, Inc.)â??indeed, more than 90% of the code is identical to the actual robot software, as will be explained later in detail. As a result, SL is divided into three software components: 1) the generic code that is shared by the actual robot and the simulation, 2) the robot specific code, and 3) the simulation specific code. The robot specific code is tailored to the robotic environments that we have experienced over the years, in particular towards VME-based multi-processor real-time operating systems. The simulation specific code has all the components for OpenGL graphics simulations and mimics the robot multi-processor environment in simple C-code. Importantly, SL can be used stand-alone for creating graphics an-imationsâ??the heritage from real-time robotics does not restrict the complexity of possible simulations. This technical report describes SL in detail and can serve as a manual for new users of SL.

am

link (url) [BibTex]

link (url) [BibTex]


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In-situ - Untersuchungen zu Interdiffusion und Magnetismus in magnetischen Multilayern

Schmidt, M.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]


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Biologically Inspired Polymer Microfibrillar Arrays for Mask Sealing

Cheung, E., Aksak, B., Sitti, M.

CARNEGIE-MELLON UNIV PITTSBURGH PA, 2009 (techreport)

pi

[BibTex]

[BibTex]


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Theorie der elektronischen Zustände in oxidischen magnetischen Materialien

Kostoglou, C.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

[BibTex]

[BibTex]


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Magnetooptische Untersuchungen an Ferromagnet- und Supraleiter-Nanosystemen und deren Hybriden

Treiber, S.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]

2006


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Minimal Logical Constraint Covering Sets

Sinz, F., Schölkopf, B.

(155), Max Planck Institute for Biological Cybernetics, Tübingen, December 2006 (techreport)

Abstract
We propose a general framework for computing minimal set covers under class of certain logical constraints. The underlying idea is to transform the problem into a mathematical programm under linear constraints. In this sense it can be seen as a natural extension of the vector quantization algorithm proposed by Tipping and Schoelkopf. We show which class of logical constraints can be cast and relaxed into linear constraints and give an algorithm for the transformation.

ei

PDF [BibTex]

2006


PDF [BibTex]


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New Methods for the P300 Visual Speller

Biessmann, F.

(1), (Editors: Hill, J. ), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2006 (techreport)

ei

PDF [BibTex]

PDF [BibTex]


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Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function

Shen, H., Jegelka, S., Gretton, A.

(PA006080), National ICT Australia, Canberra, Australia, October 2006 (techreport)

ei

Web [BibTex]

Web [BibTex]


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A tutorial on spectral clustering

von Luxburg, U.

(149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

Abstract
In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Nevertheless, on the first glance spectral clustering looks a bit mysterious, and it is not obvious to see why it works at all and what it really does. This article is a tutorial introduction to spectral clustering. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

ei

PDF [BibTex]

PDF [BibTex]


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Towards the Inference of Graphs on Ordered Vertexes

Zien, A., Raetsch, G., Ong, C.

(150), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

Abstract
We propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs.

ei

PDF [BibTex]

PDF [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.

ei

PDF Web [BibTex]

PDF Web [BibTex]