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Springer Preference Learning - Springer Machine Learning Book

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Description

Preference Learning represents a cutting-edge branch of machine learning and data mining that has gained significant traction in artificial intelligence research. This Springer publication explores how to learn from observations that reveal individual or group preferences, offering a declarative approach to specifying desires and combining qualitative and quantitative reasoning methods. The book addresses the flexible handling of inconsistencies and exceptions within preference-based systems, making it essential for researchers and practitioners working with preference elicitation, ranking systems, and decision-making algorithms. Ideal for academics, data scientists, and AI professionals seeking to understand preference representation and processing in modern machine learning applications.

Key Features

Preference Learning by Springer provides comprehensive coverage of learning from preference observations in machine learning and data mining research.

The book explores declarative preference specification methods that allow flexible combination of qualitative and quantitative reasoning approaches.

Preference Learning addresses handling inconsistencies and exceptions within preference-based systems for robust decision-making.

Springer's treatment of preference representation enables generalisation beyond training data in machine learning applications.

The publication covers preference-based systems relevant to ranking, recommendation engines, and artificial intelligence research.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
10 October 2010
Listed Since
08 June 2010

Barcode

No barcode data available

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