Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

17 results (View BibTeX file of all listed publications)

2011

JMLR Workshop and Conference Proceedings Volume 19: COLT 2011

pages: 834, MIT Press, Cambridge, MA, USA, 24th Annual Conference on Learning Theory , June 2011 (proceedings)

2011

Kakade, S., von Luxburg, U. JMLR Workshop and Conference Proceedings Volume 19: COLT 2011 pages: 834, MIT Press, Cambridge, MA, USA, 24th Annual Conference on Learning Theory , June 2011 (proceedings)

Web [BibTex]

Crowdsourcing for optimisation of deconvolution methods via an iPhone application

Lang, A.

Hochschule Reutlingen, Germany, April 2011 (mastersthesis)

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Lang, A. Crowdsourcing for optimisation of deconvolution methods via an iPhone application Hochschule Reutlingen, Germany, April 2011 (mastersthesis)

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Learning functions with kernel methods

Dinuzzo, F.

University of Pavia, Italy, January 2011 (phdthesis)

PDF [BibTex]

Dinuzzo, F. Learning functions with kernel methods University of Pavia, Italy, January 2011 (phdthesis)

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Model Learning in Robot Control

Nguyen-Tuong, D.

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

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Nguyen-Tuong, D. Model Learning in Robot Control Albert-Ludwigs-Universität Freiburg, Germany, 2011 (phdthesis)

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2009

Methods for feature selection in a learning machine

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F.

United States Patent, No 7624074, November 2009 (patent)

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2009

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F. Methods for feature selection in a learning machine United States Patent, No 7624074, November 2009 (patent)

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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.

PDF [BibTex]

Gehler, PV. Kernel Learning Approaches for Image Classification Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

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A PAC-Bayesian Approach to Structure Learning

Seldin, Y.

Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, September 2009 (phdthesis)

PDF [BibTex]

Seldin, Y. A PAC-Bayesian Approach to Structure Learning Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, September 2009 (phdthesis)

PDF [BibTex]

Acquiring web page information without commitment to downloading the web page

Heilbron, L., Platt, J. C., Simard, P. Y., Schölkopf, B.

United States Patent, No 7565409, July 2009 (patent)

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Heilbron, L., Platt, J. C., Simard, P. Y., Schölkopf, B. Acquiring web page information without commitment to downloading the web page United States Patent, No 7565409, July 2009 (patent)

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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)

PDF PDF [BibTex]

Blaschko, MB. Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

PDF PDF [BibTex]

Pre−processed feature ranking for a support vector machine

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F., Guyon, I.

United States Patent, No. 7475048, January 2009 (patent)

[BibTex]

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F., Guyon, I. Pre−processed feature ranking for a support vector machine United States Patent, No. 7475048, January 2009 (patent)

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Motor Control and Learning in Table Tennis

Mülling, K.

Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

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Mülling, K. Motor Control and Learning in Table Tennis Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

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

Drewe, P.

Eberhard Karls Universität Tübingen, Germany, 2009 (diplomathesis)

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Drewe, P. Hierarchical Clustering and Density Estimation Based on k-nearest-neighbor graphs Eberhard Karls Universität Tübingen, Germany, 2009 (diplomathesis)

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Learning with Structured Data: Applications to Computer Vision

Nowozin, S.

Technische Universität Berlin, Germany, 2009 (phdthesis)

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Nowozin, S. Learning with Structured Data: Applications to Computer Vision Technische Universität Berlin, Germany, 2009 (phdthesis)

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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)

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Steinke, F. From Differential Equations to Differential Geometry: Aspects of Regularisation in Machine Learning Universität des Saarlandes, Saarbrücken, Germany, 2009 (phdthesis)

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2000

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]

2000

Urbanek, M. Three-dimensional reconstruction of planar scenes Biologische Kybernetik, INP Grenoble, Warsaw University of Technology, September 2000 (diplomathesis)

ZIP [BibTex]