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Springer Soft Methods for Integrated Uncertainty Modelling - Springer

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Description

Soft Methods for Integrated Uncertainty Modelling explores the foundational principles of soft computing, an approach that emerged in the early 1990s from the fuzzy systems community. This book addresses how uncertainty, imprecision and ignorance in complex problems should be explicitly represented and leveraged within computational frameworks rather than eliminated or ignored. Unlike conventional hard computing, soft computing methodologies tolerate imprecision, uncertainty and partial truth, mirroring the adaptive capabilities of human reasoning. Published by Springer as part of the Advances in Intelligent and Soft Computing series (Volume 37), this text provides essential insights into integrating uncertainty handling techniques across AI and machine learning applications. Ideal for researchers, academics and practitioners seeking to understand how soft computing principles can enhance problem-solving in domains where perfect information is unavailable.

Key Features

Soft Methods for Integrated Uncertainty Modelling covers fuzzy systems and soft computing theory from the early 1990s onwards.

Volume 37 in the Advances in Intelligent and Soft Computing series published by Springer.

Explores how uncertainty, imprecision and ignorance should be explicitly represented in computational models.

Demonstrates how soft computing differs from conventional hard computing by tolerating uncertainty and partial truth.

Bridges machine learning and artificial intelligence with practical soft computing methodologies for real-world applications.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
14 August 2006
Listed Since
31 December 2006

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