1313 search results - page 80 / 263 » Dimensionality Reduction for Classification |
147
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CVPR
2004
IEEE
16 years 5 months ago
2004
IEEE
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
111
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TASLP
2002
15 years 2 months ago
2002
Two approaches are proposed for the design of tied-mixture hidden Markov models (TMHMM). One approach improves parameter sharing via partial tying of TMHMM states. To facilitate ty...
132
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ICML
2003
IEEE
16 years 4 months ago
2003
IEEE
We investigate how random projection can best be used for clustering high dimensional data. Random projection has been shown to have promising theoretical properties. In practice,...
129
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ICONIP
2007
15 years 4 months ago
2007
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
102
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AAAI
2008
15 years 5 months ago
2008
We are interested in the problem of reasoning over very large common sense knowledge bases. When such a knowledge base contains noisy and subjective data, it is important to have ...