• Title Machine Learning, Neural and Statistical Classification
• Author(s) Donald Michie, David Spiegelhalter, Charles Taylor
• Publisher: Prentice Hall (July 1994); Overseas Press 2009 edition (August 28, 2009)
• Hardcover 289 pages
• Paperback 290 pages
• eBook Online, HTML, PDF (1.7 MB)
• Language: English
• ISBN-10: 8188689734 (1004 Edition: 013106360X)
• ISBN-13: 978-8188689736 (1994 Edition: 978-0131063600)
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Book Description

Statistical, machine learning and neural network approaches to classification are all covered in this volume. Contributions have been integrated to provide an objective assessment of the potential for machine learning algorithms in solving significant commercial and industrial problems, widening the foundation for exploitation of these and related algorithms.

This Volume Was Written As A Result of The statlog Project, Funded Under The Esprit Programme of The European Union. In Addition to The Experimental Results, The Project Had The Desirable Effect of Encouraging Collaboration, Overdue In This Field, Between Workers In Different Disciplines. The Intersection of, And Interaction Between Machine Learning And Statistics Is Now ARapidly Growing Area of Interest. There Are Obvious Areas of Common Research, The Main One Being Classification, But Communication Has Been Hampered By Use of Different Language And Terminology.

In This Volume, Statisticians, Ai Workers In Machine Learning, And Neural Net Specialists Have Come Together In New Patterns of Interaction And Collaboration. We Offer This Book As A Source of Useful Information For Workers In Medicine, Agriculture, Industry, Finance And Other Applied Studies. We Also Hope That It May Contribute To The Spread of Similar Collaborations In The Scientific Community At Large, As Well As Further Research At The Interface of Machine Learning And Statistics.

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• Machine Learning
• Neural Networks and Deep Learning
• Statistics, R Language and SAS Programming
• Artificial Intelligence and Logic Programming

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