Isbn: 9780471681823 - learning from data: concepts, theory, and methods (ieee press) (12 Ergebnisse)

Zustand: Good. Good condition. 2nd edition. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.

Hardcover. Zustand: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Hardcover. Zustand: Gut. Second edition. 560 pp. Cover sun-bleached at spine, otherwise very well preserved copy 324 Sprache: Englisch Gewicht in Gramm: 1200.

hardcover. Zustand: Gut. 560 Seiten; 9780471681823.3 Gewicht in Gramm: 1.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. xviii + 538 Illus.

Zustand: New. In English.

Zustand: New. Vladimir CherKassky, PhD, is Professor of Electrical and Computer Engineering at the University of Minnesota. He is internationally known for his research on neural networks and statistical learning.Filip Mulier, PhD, has worked in the software field for th.

Hardcover. Zustand: Brand New. 2nd edition. 538 pages. 9.75x6.50x1.25 inches. In Stock.

Zustand: New. 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. Num Pages: 538 pages, Illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 244 x 157 x 38. Weight in Grams: 906. . 2007. 2nd Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Buch. Zustand: Neu. Neuware - An interdisciplinary framework for learning methodologies-now revised and updatedLearning from Data 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 pattern recognition can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science.Since the first edition was published, the field of data-driven learning has experienced rapid growth. This Second Edition covers these developments with a completely revised chapter on support vector machines, a new chapter on noninductive inference and alternative learning formulations, and an in-depth discussion of the VC theoretical approach as it relates to other paradigms.Complete with over one hundred illustrations, case studies, examples, and chapter summaries, Learning from Data accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.…