Machine Learning under a Modern Optimization Lens, 最新の最適化レンズで見た機械学習, 9781733788502, 978-1-73378-850-2
Description
The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be optimal in minutes/hours, and outperform classical heuristic approaches in out-of-sample experiments.
This book was awarded the 2021 INFORMS Frederick W. Lanchester Prize, which recognizes the best contribution to operations research and the management sciences published in English in the past five years. The Lanchester Prize, established in 1954, is the highest honor bestowed by INFORMS.
Contents:
Part I covers robust, sparse, nonlinear, holistic regression andextensions.
Part II contains optimal classification and regression trees.
Part III outlines prescriptive MLmethods.
Part IV shows the power of optimization over randomization in design of experiments, exceptional responders, stable regression and the bootstrap.
Part V describes unsupervised methods in ML: optimal missing data imputation and interpretable clustering.
Part VI develops matrix ML methods: sparse PCA, sparse inversecovariance estimation, factor analysis, matrix and tensor completion.
Part VII demonstrates how ML leads to interpretable optimization.