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Item Details
| Title:
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LEARNING FROM DATA
CONCEPTS, THEORY, AND METHODS |
| By: |
Vladimir Cherkassky, Filip M. Mulier |
| Format: |
Hardback |
| List price:
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£140.95 |
| Our price: |
£126.86 |
| Discount: |
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| You save:
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£14.09 |
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| ISBN 10: |
0471681822 |
| ISBN 13: |
9780471681823 |
| Availability: |
Usually dispatched within 1-3 weeks.
 Delivery
rates
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| Stock: |
Currently 0 available |
| Publisher: |
JOHN WILEY AND SONS LTD |
| Pub. date: |
24 August, 2007 |
| Edition: |
2nd Revised edition |
| Pages: |
538 |
| Description: |
An interdisciplinary framework for learning methodologies, covering statistics, neural networks, and fuzzy logic, Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data. |
| Synopsis: |
An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text. |
| Illustrations: |
Illustrations |
| Publication: |
UK |
| Imprint: |
Wiley-Blackwell (an imprint of John Wiley & Sons Ltd) |
| Returns: |
Returnable |
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