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Chapman and Hall/CRC Symbolic Regression Machine Learning Book

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Description

Explore the capabilities of Symbolic Regression (SR) with this in-depth guide from Chapman and Hall/CRC. As one of the most powerful machine learning techniques, SR produces transparent models by searching the space of mathematical expressions to represent relationships between predictors and dependent variables. This approach removes the need for assumptions about model structure. This book focuses on the most prevalent learning algorithms for SR, which are based on genetic programming (GP). You will learn about these evolutionary algorithms inspired by the principles of natural selection. The text provides a detailed guide to GP for SR, covering advanced techniques used to develop mathematical models through evolutionary processes.

Key Features

This book by Chapman and Hall/CRC provides an in-depth guide to genetic programming for symbolic regression.

Learn how to produce transparent machine learning models that represent relationships without assuming model structures.

The text covers genetic programming algorithms inspired by the principles of natural selection.

Explore advanced techniques for searching mathematical expression spaces to find optimal models.

Understand the application of evolutionary algorithms to solve complex predictive modeling problems.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 July 2024
Listed Since
25 January 2024

Barcode

No barcode data available

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