Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
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Neural Networks for Pattern Recognition
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Learning Mixtures of Gaussians
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A spectral algorithm for learning mixture models
Journal of Computer and System Sciences - Special issue on FOCS 2002
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
The spectral method for general mixture models
COLT'05 Proceedings of the 18th annual conference on Learning Theory
On spectral learning of mixtures of distributions
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An outlier-aware data clustering algorithm in mixture models
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A spectral algorithm for learning Hidden Markov Models
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Learning mixtures of arbitrary distributions over large discrete domains
Proceedings of the 5th conference on Innovations in theoretical computer science
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We show that, given data from a mixture of k well-separated spherical Gaussians in ℜd, a simple two-round variant of EM will, with high probability, learn the parameters of the Gaussians to near-optimal precision, if the dimension is high (d ln k). We relate this to previous theoretical and empirical work on the EM algorithm.