Algebraic Geometry and Statistical Learning Theory
by Sumio Watanabe 2020-07-23 16:08:56
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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-f... Read more
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. Less
  • File size
  • Print pages
  • Publisher
  • Publication date
  • ISBN
  • 9x6x0.8inches
  • 286
  • Cambridge University Press
  • August 1, 2009
  • 9780521864671
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