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Springer Towards Advanced Data Analysis Soft Computing 285

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Description

Explore the intersection of engineering and mathematics with Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Published by Springer as part of the Studies in Fuzziness and Soft Computing series, this volume addresses the distinct methodologies used to interpret complex data sets. Soft computing provides a flexible approach to data analysis by focusing on quick, working solutions through approximations and unconventional methods. This makes it highly effective for creating models that adapt to specific application needs, especially when dealing with imprecision or uncertainty. These models are designed to be intuitive and interpretable for practical use. In contrast, statistics offers a rigorous mathematical framework aimed at establishing objective conclusions from experimental data. By examining how these two fields interact, this book provides a comprehensive look at how different scientific disciplines approach data. It is a valuable resource for those studying computer science, AI, and machine learning who want to understand how to balance flexibility with mathematical rigor.

Key Features

Examines the relationship between soft computing as an engineering science and statistics as a classical branch of mathematics.

Covers soft computing methods that prioritize flexibility and the creation of models suited for specific application needs.

Explains how to utilize approximations and unconventional approaches to reach working solutions quickly.

Provides insight into developing intuitive and interpretable models that handle uncertainty and imprecision.

Discusses the rigorous statistical methods used to reach objective conclusions based on experimental data.

Part of the Studies in Fuzziness and Soft Computing series (Volume 285) from Springer.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
29 August 2012
Listed Since
13 April 2012

Barcode

No barcode data available

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