# 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**

**Multilinear Subspace Learning:
Dimensionality Reduction
of Multidimensional Data**

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.

**Computational Business Analytics**

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.

**Multi-Label Dimensionality Reduction**

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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