Synopsis
Part 1: Background The logic of induction - Richard Forsyth Machine induction as a form of knowledge acquisition in knowledge engineering - Anna Hart Inductive learning: the user's perspective - Tomasz Arciszewski and M. Mustafa Part 2: Biologically inspired systems The evolution of intelligence - Richard Forsyth Artificial evolution and artificial intelligence - Ingo Rechenberg Learning and distributed memory: getting close to neurons - Igor Aleksander Part 3: Automated discovery Automated discovery - Kenneth Haase The acquisition of natural language by machine - Chris Naylor A computational model of creativity - Masoud Yazdani Part 4: Long-term perspectives The road to knowledge-rich learning - Roy Rada Databases that learn - Derek Partridge Cognitive architecture and connectionism - Ajit Narayanan Machine learning: the next ten years - Dimitris Chorafas.
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Machine Learning: Principles and Techniques (Chapman and Hall Computing Series)
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Machine Learning: Principles and Techniques (Chapman and Hall Computing Series)
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Condition: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,450grams, ISBN:9780412305801. Seller Inventory # 4314307
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MACHINE LEARING: PRINCIPLES AND TECHNIQUES
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Machine Learning: Principles and Techniques (Chapman and Hall Computing Series)
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Soft cover. Condition: Fine. 8vo (23 cm), XV, 255 pp. Laminated wrappers (ownership entry on title). "This book aims to present the key findings of machine learning research, explained by experts in the field, in an accessible manner for both students and computer professionals. The authors attempt to convey some of the excitement of working in this dynamic area at the frontier of computer science. They also show how the techniques described can lead to practical results." (from the blurb). Seller Inventory # 008314
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