Algebraic Geometry and Statistical Learning Theory

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文件名称：Algebraic Geometry and Statistical Learning Theory
文件大小：1.71MB
文件格式：PDF
更新时间：2013-06-27 16:32:39
Algebraic Geometry Statistical Learning Theory Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.