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Springer Preference Learning - Machine Learning and AI Book

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Description

Explore the growing field of preference learning with this comprehensive text from Springer. As a new branch of machine learning and data mining, preference learning has become a major area of interest in artificial intelligence research. This book focuses on how to learn from observations that reveal information about the preferences of individuals or specific classes of individuals. By representing and processing knowledge through preferences, researchers can specify desires in a declarative way. This approach allows for the combination of qualitative and quantitative reasoning modes. It also provides a flexible way to handle inconsistencies and exceptions within data sets. This text is designed to help readers understand how to generalize beyond training data to build more effective AI models.

Key Features

Covers preference learning as a specialized branch of machine learning and data mining research.

Explains how to derive information about individual or class preferences from observations.

Provides methods for specifying desires using declarative knowledge representation.

Integrates qualitative and quantitative modes of reasoning for advanced data processing.

Offers flexible approaches to managing inconsistencies and exceptions in AI models.

Focuses on the ability to generalize knowledge beyond standard training data.

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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