TAILIEUCHUNG - Data Mining and Knowledge Discovery Handbook, 2 Edition part 11

Data Mining and Knowledge Discovery Handbook, 2 Edition part 11. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 80 Christopher . Burges as radial basis function kernels this centering is equivalent to centering a distance matrix in feature space. Williams 2001 further points out that for these kernels classical MDS in feature space is equivalent to a form of metric MDS in input space. Although ostensibly kernel PCA gives a function that can be applied to test points while MDS does not kernel PCA does so by using the Nystrom approximation see Section and exactly the same can be done with MDS. The subject of feature extraction and dimensional reduction is vast. In this review I ve limited the discussion to mostly geometric methods and even with that restriction it s far from complete so I d like to alert the reader to three other interesting leads. The first is the method of principal curves where the idea is to find that smooth curve that passes through the data in such a way that the sum of shortest distances from each point to the curve is minimized thus providing a nonlinear one-dimensional summary of the data Hastie and Stuetzle 1989 the idea has since been extended by applying various regularization schemes including kernel-based and to manifolds of higher dimension Scholkopf and Smola 2002 . Second competitions have been held at recent NIPS workshops on feature extraction and the reader can find a wealth of information there Guyon 2003 . Finally recent work on object detection has shown that boosting where each weak learner uses a single feature can be a very effective method for finding a small set of good and mutually complementary features from a large pool of possible features Viola and Jones 2001 . Acknowledgments I thank John Platt for valuable discussions. Thanks also to Lawrence Saul Bernhard Scholkopf Jay Stokes and Mike Tipping for commenting on the manuscript. References . Aizerman . Braverman and . Rozoner. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25 821-837 .

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