[PDF.66vs] Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing series)

Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing series)

[PDF.qm27] Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing series)

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 | #2659076 in Books |  2006-03-24 | Original language:English | PDF # 1 |  10.00 x.75 x6.00l,1.75 | File type: PDF | 280 pages

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||About the Author||Trevor Darrell is Associate Professor and Head of the Vision Interface Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.

|Piotr Indyk is Associate Professor in the Theory of Computation Group in the Comput

Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as a...

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