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Machine learning : principles and techniques

Author: Richard Forsyth
Publisher: London ; New York : Chapman and Hall, 1989.
Series: Chapman and Hall computing series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Presents results of research into computer systems that can improve their own performance. For undergraduates, graduates, and professionals intending to write or use such systems. The various perspectives of over a dozen contributors are abstracted into the unifying principle: generate + test, which makes possible a provisional taxonomy of machine.
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Genre/Form: Aufsatzsammlung
Additional Physical Format: Online version:
Machine learning.
London ; New York : Chapman and Hall, 1989
(OCoLC)568742961
Document Type: Book
All Authors / Contributors: Richard Forsyth
ISBN: 0412305704 9780412305702 0412305801 9780412305801
OCLC Number: 18255812
Description: xv, 255 pages : illustrations ; 25 cm.
Contents: 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.
Series Title: Chapman and Hall computing series.
Responsibility: edited by Richard Forsyth.

Abstract:

Presents results of research into computer systems that can improve their own performance. For undergraduates, graduates, and professionals intending to write or use such systems. The various perspectives of over a dozen contributors are abstracted into the unifying principle: generate + test, which makes possible a provisional taxonomy of machine.

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