Header logo is ei


2012


no image
Scalable graph kernels

Shervashidze, N.

Eberhard Karls Universität Tübingen, Germany, October 2012 (phdthesis)

Web [BibTex]

2012

Web [BibTex]


no image
Probabilistic Modelling of Expression Variation in Modern eQTL Studies

Zwießele, M.

Eberhard Karls Universität Tübingen, Germany, October 2012 (mastersthesis)

[BibTex]

[BibTex]


no image
Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

PDF [BibTex]

PDF [BibTex]


no image
Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Web [BibTex]


no image
Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

Web [BibTex]

Web [BibTex]


no image
Machine Learning and Interpretation in Neuroimaging - Revised Selected and Invited Contributions

Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B.

pages: 266, Springer, Heidelberg, Germany, International Workshop, MLINI, Held at NIPS, 2012, Lecture Notes in Computer Science, Vol. 7263 (proceedings)

DOI [BibTex]

DOI [BibTex]


no image
MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication)

Panagiotaki, E., O’Donnell, L., Schultz, T., Zhang, G.

15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI , 2012 (proceedings)

PDF [BibTex]

PDF [BibTex]


no image
Mining correlated loci at a genome-wide scale

Velkov, V.

Eberhard Karls Universität Tübingen, Germany, 2012 (mastersthesis)

[BibTex]

[BibTex]

2011


no image
JMLR Workshop and Conference Proceedings Volume 19: COLT 2011

Kakade, S., von Luxburg, U.

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

Web [BibTex]

2011

Web [BibTex]


no image
Crowdsourcing for optimisation of deconvolution methods via an iPhone application

Lang, A.

Hochschule Reutlingen, Germany, April 2011 (mastersthesis)

[BibTex]


no image
Learning functions with kernel methods

Dinuzzo, F.

University of Pavia, Italy, January 2011 (phdthesis)

PDF [BibTex]

PDF [BibTex]


no image
Model Learning in Robot Control

Nguyen-Tuong, D.

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

[BibTex]

[BibTex]

2005


no image
Extension to Kernel Dependency Estimation with Applications to Robotics

BakIr, G.

Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

Abstract
Kernel Dependency Estimation(KDE) is a novel technique which was designed to learn mappings between sets without making assumptions on the type of the involved input and output data. It learns the mapping in two stages. In a first step, it tries to estimate coordinates of a feature space representation of elements of the set by solving a high dimensional multivariate regression problem in feature space. Following this, it tries to reconstruct the original representation given the estimated coordinates. This thesis introduces various algorithmic extensions to both stages in KDE. One of the contributions of this thesis is to propose a novel linear regression algorithm that explores low-dimensional subspaces during learning. Furthermore various existing strategies for reconstructing patterns from feature maps involved in KDE are discussed and novel pre-image techniques are introduced. In particular, pre-image techniques for data-types that are of discrete nature such as graphs and strings are investigated. KDE is then explored in the context of robot pose imitation where the input is a an image with a human operator and the output is the robot articulated variables. Thus, using KDE, robot pose imitation is formulated as a regression problem.

PDF PDF [BibTex]

2005

PDF PDF [BibTex]


no image
Geometrical aspects of statistical learning theory

Hein, M.

Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

PDF [BibTex]

PDF [BibTex]


no image
Implicit Surfaces For Modelling Human Heads

Steinke, F.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

[BibTex]

[BibTex]


no image
Machine Learning Methods for Brain-Computer Interdaces

Lal, TN.

Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

Web [BibTex]

Web [BibTex]


no image
Liver Perfusion using Level Set Methods

Nowozin, S.

Biologische Kybernetik, Shanghai JiaoTong University, Shanghai, China, July 2005 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


no image
Discriminative Methods for Label Sequence Learning

Altun, Y.

Brown University, Providence, RI, USA, May 2005 (phdthesis)

PDF [BibTex]

PDF [BibTex]


no image
Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, TG.

Biologische Kybernetik, Eberhard-Karls University Tübingen, Tübingen, Germany, May 2005 (diplomathesis)

Abstract
A common task in psychophysics is to measure the psychometric function. A psychometric function can be described by its shape and four parameters: offset or threshold, slope or width, false alarm rate or chance level and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. A new Bayesian adaptive psychometric method placing trials by minimising the expected entropy of the posterior probabilty dis- tribution over a set of possible stimuli is introduced. The method is more flexible, faster and at least as efficient as the established method (Kontsevich and Tyler, 1999). Comparably accurate (2dB) threshold and slope estimates can be obtained after about 30 and 500 trials, respectively. By using a dynamic termination criterion the efficiency can be further improved. The method can be applied to all experimental designs including yes/no designs and allows acquisition of any set of free parameters. By weighting the importance of parameters one can include nuisance parameters and adjust the relative expected errors. Use of nuisance parameters may lead to more accurate estimates than assuming a guessed fixed value. Block designs are supported and do not harm the performance if a sufficient number of trials are performed. The method was evaluated by computer simulations in which the role of parametric assumptions, its robustness, the quality of different point estimates, the effect of dynamic termination criteria and many other settings were investigated.

[BibTex]

[BibTex]


no image
Support Vector Classification of Images with Local Features

Blaschko, MB.

Biologische Kybernetik, University of Massachusetts, Amherst, May 2005 (diplomathesis)

[BibTex]

[BibTex]


no image
Efficient Pattern Selection for Support Vector Classifiers and its CRM Application

Shin, H.

Biologische Kybernetik, Seoul National University, Seoul, Korea, February 2005 (phdthesis)

PDF [BibTex]

PDF [BibTex]


no image
Kernels: Regularization and Optimization

Ong, CS.

Biologische Kybernetik, The Australian National University, Canberra, Australia, 2005 (phdthesis)

PDF GZIP [BibTex]

PDF GZIP [BibTex]