Algebraic Geometry and Statistical Learning Theory 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Algebraic Geometry and Statistical Learning Theory

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Sumio Watanabe 作者
Cambridge University Press
译者
2009-8-13 出版日期
300 页数
GBP 57.00 价格
Hardcover
丛书系列
9780521864671 图书编码

Algebraic Geometry and Statistical Learning Theory 在线电子书 图书标签: 统计学习  代数几何  机器学习  数学  计算机科学  统计  数学-AlgebraicGeometry  计算机-ai   


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发表于2024-12-25


Algebraic Geometry and Statistical Learning Theory 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Algebraic Geometry and Statistical Learning Theory 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Algebraic Geometry and Statistical Learning Theory 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Algebraic Geometry and Statistical Learning Theory 在线电子书 用户评价

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这才是数学化的统计

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这才是数学化的统计

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这才是数学化的统计

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这才是数学化的统计

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这才是数学化的统计

Algebraic Geometry and Statistical Learning Theory 在线电子书 著者简介

Sumio Watanabe is a Professor in the Precision and Intelligence Laboratory at the Tokyo Institute of Technology.


Algebraic Geometry and Statistical Learning Theory 在线电子书 图书目录


Algebraic Geometry and Statistical Learning Theory 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Algebraic Geometry and Statistical Learning Theory 在线电子书 图书描述

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.

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