Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)
Product Description
Condition - Very Good
The item shows wear from consistent use but remains in good condition. It may arrive with damaged packaging or be repackaged.
Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)
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.
Technical Specifications
You might also like
Information Geometry and Its Applications (Applied Mathematical Sciences)
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
Advances in Financial Machine Learning
Methods of Information Geometry (Translations of Mathematical Monographs) (Tanslations of Mathematical Monographs)
Deep Learning (Adaptive Computation and Machine Learning series)
High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Information Geometry (Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics)
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics)
Algorithms for Optimization (The MIT Press)