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146
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AAAI
2010
15 years 5 months ago
2010
Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be...
128
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ESANN
2007
15 years 5 months ago
2007
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
147
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ESANN
2007
15 years 5 months ago
2007
We study dimensionality reduction or feature selection in text document categorization problem. We focus on the first step in building text categorization systems, that is the cho...
160
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NIPS
2008
15 years 5 months ago
2008
Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches such as exponential ...
121
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NIPS
2004
15 years 5 months ago
2004
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic v...