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

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ADAGE

Active Support Vector Learning with Statistical Queries, mining applications. The learning strategy is motivated by the statistical query model. While most existing methods of active SVM learning query for points based on their proximity to the current separating hyperplane, the proposed method queries for a set of points according to a distribution as d

河流

Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine,, and the Linear Discriminant Analysis (LDA). As a unified approach, M. combines some merits from these three models. While LDA and MPM focus on building the decision plane using global information and SVM focuses on constructing the decision plane in a local manner, M. incorporates these two seemin

Dorsal-Kyphosis

Active-Set Methods for Support Vector Machines,urrently, most SVM optimizers implement working-set (decomposition)techniques because of their ability to handle large data sets. Although these show good results in general, active-set methods are a reasonable alternative - in particular if the data set is not too large, if the problem is ill-condi

苦笑

Theoretical and Practical Model Selection Methods for Support Vector Classifiers,ning but are not fully justified by a theoretical point of view; on the other hand, some methods rely on rigorous theoretical work but are of little help when applied to real-world problems, because the underlying hypotheses cannot be verified or the result of their application is uninformative. Our

obstinate

Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification,e curse-of-dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose locally adaptive nearest neighbor classification methods to try to minimize bias. We use locally linear support vector machines as well as quasiconformal transformed kerne

PLE

Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods,ing the Riemannian metric in the neighborhood of the boundary, thereby increasing separation between the classes. The second method is concerned with optimal location of the separating boundary, given that the distributions of data on either side may have different scales.

Mawkish

An Accelerated Robust Support Vector Machine Algorithm,em when there is outlier in the training data set, which makes the decision surface less detored and results in sparse support vectors. Training of the robust SVM leads to a quadratic optimization problem with bound and linear constraint. Osuna provides a theorem which proves that the Standard SVM’s

prostate-gland

obscurity

Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and PerforSVMs) problems. First, the equality of a Kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and the Sequential Minimal Optimization (SMO) learning algorithm (based on an analytic quadratic programming step for a model without bias term .) in designing SVMs with positiv