Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (Technical Report)
In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In
contrast to existing approaches which are non-convex and/or generative we propose to use a discriminative model leading to convex problem formulation and complexity control.
Furthermore we assert that decision functions should not compare apples and oranges by comparing features of different types directly. Instead we propose to combine different similarity measures for each different
feature type. Furthermore we argue that the question: Which feature type is more discriminative for task X? is ill-posed and show empirically that the answer to this question might depend on the complexity of the decision function.
In Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, Advances in Neural Information Processing Systems 16, pages: 1367-1374, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (Conference Paper)
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a population of simultaneously recorded neurons. We compare several ways of improving the coding of the input (i.e., the spike data) as well as of the output (i.e., the orientation), and report the results obtained using different kernel algorithms.
(137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (Technical Report)
In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.
Technische Universität Dresden, Dresden/Germany, May 2001 (Thesis)
Using different modifications of a new variational approach, statical
groundstate properties of the one-band Hubbard model such as energy
and staggered magnetisation are calculated. By taking into account
additional fluctuations, the method ist gradually improved so that a
very good description of the energy in one and two dimensions can be
achieved. After a detailed discussion of the application in one dimension, extensions for two dimensions are introduced. By use of a modified
version of the variational ansatz in particular a description of the
quantum phase transition for the magnetisation should be possible.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems