Titlebook: Support Vector Machines: Theory and Applications; Lipo Wang Book 2005 Springer-Verlag Berlin Heidelberg 2005 Data Mining.Fuzzy.Kernel Mach

Minuet

Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine,ctives on the three models leading up to the development of M.. We then outline the M. framework and perform investigations on various aspects including the mathematical definition, the geometrical interpretation, the time complexity, and its relationship with other existing models.

coalition

Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification,t discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a variety of data sets.

同义联想法

An Accelerated Robust Support Vector Machine Algorithm,composition algorithm for the robust SVM. Furthermore, a pre-selection technique is incorporated into the algorithm to speed up the calculation. The experiment using standard data sets shows that the accelerated decomposition algorithm makes the training process more efficient.

外露

Kernel Discriminant Learning with Application to Face Recognition, results indicate that the method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel Principal Component Analysis and kernel Linear Discriminant Analysis, at a significantly reduced computational cost.

Peak-Bone-Mass

Mosaic

V. Kecmanprojects created a growing demand of computational resources for their management and analysis thus giving rise to an interdisciplinary field between computer sciences and molecular biology. This point of view is not completely satisfactory. First, it precludes the historical background of a field t

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内部

K. Pelckmans,I. Goethals,J.D. Brabanter,J.A.K. Suykens,B.D. Moorof this fact. The probability that the verifier accepts an input in the language given a correct proof is called the .; the probability that the verifier rejects an input not in the language given any proof is called the .. For a verifier posing . queries to the proof, the . is defined by . / log.(.

合适

Debark

K. Huang,H. Yang,I. King,M.R. Lyus work we consider the uniform distribution over .-colorable graphs with . vertices and exactly . edges, . greater than some sufficiently large constant. We rigorously show that all proper .-colorings of most such graphs are clustered in one cluster, and agree on all but a small, though constant, nu