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Posted by
btel
- Last updated: 2004-07-06
| Title |
Article.2004.07.06.10
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| File | SVM2002.pdf |
| Short Description |
Sparse correlation kernel analysis and evolutionary algorithm-based modeling of the sensory activity within the rat's barrel cortex.
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| Description | This paper presents a new paradigm for signal decomposition and reconstruction that is based on the selection of a sparse set of basis functions. Based on recently reported results, we note that this frame-work is equivalent to approximating the signal using Support Vector Machines. Two different algorithms of modeling sensory activity within the barrel cortex of a rat are presented. First, a slightly modified ap-proach to the Independent Component Analysis (ICA) algorithm and its application to the investigation of Evoked Potentials (EP), and sec-ond, an Evolutionary Algorithm (EA) for learning an overcomplete basis of the EP components by viewing it as probabilistic model of the ob-served data. The results of the experiments conducted using these two approaches as well as a discussion concerning a possible utilization of those results are also provided. |
| Bibliographic Information | Proc. of the International Workshop on Pattern Recognition with Support Vector Machines (SVM2002), pp. 198-212, Niagara Falls, Canada, 2002 |
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| Original Release Date |
2004/04/19 20:26:00 GMT+2
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