£48.06

Springer - Rule Extraction from Support Vector Machines (Vol 80)

3187102

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Description

Support vector machines (SVMs) represent one of the most active research areas in machine learning today. While SVMs demonstrate strong performance in various applications, such as text and image classification, their learning capability presents a significant challenge: an inherent inability to explain how a specific learning result was reached in a comprehensible form. This volume, part of the Studies in Computational Intelligence series, addresses this gap in explainability. Much like the challenges faced with neural networks, the lack of explanation capability in SVMs has led to new research directions. This book explores approaches aimed at extracting symbolic rules from these models to make their processes more transparent. By focusing on rule extraction, this text provides insights into making machine learning results more understandable. It is a specialized resource for those studying the intersection of computational intelligence and model interpretability, helping to bridge the gap between high-performance machine learning and human-readable logic.

Key Features

Explores the research area of Support Vector Machines (SVMs) within the field of machine learning.

Addresses the challenge of explainability in SVMs, which often lack a comprehensible way to show how results are reached.

Provides insights into applications of SVMs, including text and image classification tasks.

Discusses methods for extracting symbolic rules to improve the transparency of machine learning processes.

Part of the Studies in Computational Intelligence series, specifically volume 80.

Offers a technical perspective on making complex models more understandable, similar to approaches used for neural networks.

Product Specifications

Model
3187102
Format
hardcover
Domain
Amazon UK
Release Date
04 January 2008
Listed Since
17 September 2007

Barcode

No barcode data available

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