New Data Mining and Machine Learning books from CRC Press – Save 25%
Save 25% on new Data Mining and Machine Learning books, including Multilinear Subspace Learning, Bayesian Programming, Computational Business Analytics, and Multi-Label Dimensionality Reduction.
Save 25% with Promo Code: MVM25
Content: 296 Pages | 56 Illustrations
Authors: Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos
Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors.
It covers the fundamentals, algorithms, and applications of MSL.
Content: 380 Pages | 61 Illustrations
Authors: Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha
Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications.
Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.
Content: 516 Pages | 290 Illustrations
Author: Subrata Das
Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains.
Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.
Content: 208 Pages | 23 Illustrations
Authors: Liang Sun, Shuiwang Ji, Jieping Ye
The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications.
Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms.
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