£98.82

Springer Soft Computing for Data Analytics and Classification

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Description

Explore the practical applications of soft computing through this comprehensive guide from Springer. This book provides a detailed look at various soft computing approaches and how they function within data analytics, classification models, and control systems. It serves as a technical resource for understanding the implementation of fuzzy logic to create advanced hybrid fuzzy driven optimization methods. The text covers a wide spectrum of essential techniques used in modern computing. Readers will find discussions on Fuzzy Logic, Rough Sets, Neutrosophic Sets, and Type-2 Fuzzy logic. Additionally, the book examines the role of Neural Networks, Generative Adversarial Networks, and Evolutionary Computation. By examining these methods across a variety of applications, this volume provides a foundation for working with complex data environments and control mechanisms. This is a valuable addition to the Studies in Fuzziness and Soft Computing series for those studying AI and machine learning.

Key Features

Covers essential soft computing techniques including Fuzzy Logic, Rough Sets, and Neutrosophic Sets for diverse computational tasks.

Explains the implementation of Type-2 Fuzzy logic and advanced hybrid fuzzy driven optimization methods.

Provides technical insights into Neural Networks and Generative Adversarial Networks for modern data science applications.

Discusses the use of Evolutionary Computation to solve problems in data analytics and control systems.

Applies various soft computing methods to real-world scenarios involving classification models and data analysis.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 January 2022
Listed Since
27 August 2020

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