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Information Science Reference Machine Learning for Commonsense Reasoning - ISR Book

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Description

Explore the intersection of machine learning and human-like reasoning with Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models. This academic resource from Information Science Reference examines how machine learning algorithms can be reformulated to approximate classification processes, mirroring human commonsense reasoning. By viewing classification as a primary mechanism for reasoning, this book transforms traditional approaches to data and knowledge communications. It provides a specialized perspective on how machines can better understand and navigate complex information sets. This text is designed for researchers and professionals looking to understand the evolution of machine learning through the lens of interactive models and advanced classification techniques. It serves as a significant reference for those studying computer science architecture and the development of intelligent systems.

Key Features

Provides a unique perspective on classification as a method for achieving human-like commonsense reasoning.

Examines the reformulation of machine learning problems into searching for the best approximations of classification.

Offers insights into how traditional data and knowledge communications are transformed by interactive models.

Contains leading research derived from international investigations in the field of machine learning.

Serves as a premier reference source for computer science and machine learning research professionals.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 October 2009
Listed Since
23 May 2009

Barcode

No barcode data available

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