£116.99

Springer - Strength or Accuracy: Credit Assignment in LCS

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Description

Explore the complex mechanics of machine learning with Strength or Accuracy: Credit Assignment in Learning Classifier Systems. This distinguished dissertation provides a deep look into classifier systems, an approach used for a wide range of machine learning problems through the automated generation and evaluation of condition/action rules. In reinforcement learning tasks, these systems work to address the two major challenges of learning a policy and generalizing over it, including related objects like value functions. While classifier systems have seen mixed success over several decades of research, this text examines the reasons behind those results. It specifically covers the developments following Stewart Wilson's 1995 claim regarding the XCS system, which introduced a different approach compared to earlier models. This book serves as a technical resource for those studying the evolution and mechanics of AI and machine learning architectures.

Key Features

Examines the automated generation and evaluation of condition/action rules within classifier systems.

Provides analysis of reinforcement learning tasks, focusing on policy learning and generalization.

Covers the relationship between learning policies and related objects such as value functions.

Discusses the historical research and the mixed success of classifier systems over 20 years.

Details the differences between the XCS system and earlier machine learning models.

Part of the Distinguished Dissertations series published by Springer London Ltd.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
20 January 2004
Listed Since
10 December 2006

Barcode

No barcode data available

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