Algebraic Geometry and Statistical Learning Theory
Sumio Watanabe
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.
年:
2009
出版社:
Cambridge University Press
语言:
english
页:
300
ISBN 10:
0521864674
ISBN 13:
9780521864671
系列:
Cambridge Monographs on Applied and Computational Mathematics
文件:
PDF, 1.71 MB
IPFS:
,
english, 2009