£112.79

Springer Genetic Programming for Image Classification Book

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Description

Explore new frontiers in computer vision with Genetic Programming for Image Classification: An Automated Approach to Feature Learning. Published by Springer, this book addresses one of the most significant challenges in machine learning: the difficulty of feature learning due to high image variations. This text introduces several new Genetic Programming (GP) approaches designed to automate the process of feature learning for image classification tasks. As an evolutionary computation technique, GP can automatically evolve computer programs to solve complex problems, making it a powerful tool for researchers and developers working in AI and machine learning. Because image classification is a fundamental step in many computer vision applications, understanding automated feature learning is essential. This book fills a significant gap in the current literature, as it is the first publication dedicated specifically to the intersection of GP and image classification. It provides a specialized resource for those looking to advance their knowledge in adaptation, learning, and optimization.

Key Features

Introduces new Genetic Programming approaches specifically designed for automated feature learning in image classification tasks.

Addresses the challenges of high image variation by using evolutionary computation techniques to evolve effective computer programs.

Provides a specialized resource for researchers in computer vision and machine learning seeking automated solutions for image analysis.

Fills a unique gap in academic literature as the first book focused on the intersection of GP and image classification.

Part of the Adaptation, Learning, and Optimization series, offering deep insights into evolutionary computation methods.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
09 February 2021
Listed Since
12 November 2020

Barcode

No barcode data available

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