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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书目名称Support Vector Machines: Theory and Applications被引频次




书目名称Support Vector Machines: Theory and Applications被引频次学科排名




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书目名称Support Vector Machines: Theory and Applications年度引用学科排名




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Malcontent

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.

Substance-Abuse

Support Vector Machines for Signal Processing,itically discusses the main difficulties related with its application to such a general set of problems. Moreover, the problem of digital channel equalization is also discussed in details since it is an important example of the use of the SVM algorithm in the signal processing.

Anticonvulsants

Cancer Diagnosis and Protein Secondary Structure Prediction Using Support Vector Machines,and protein secondary structure prediction (PSSP). For the problem of cancer diagnosis, the SVMs that we used achieved highly accurate results with fewer genes compared to previously proposed approaches. For the problem of PSSP, the SVMs achieved results comparable to those obtained by other methods.

Baffle

Studies in Fuzziness and Soft Computinghttp://image.papertrans.cn/t/image/882149.jpg

有偏见

https://doi.org/10.1007/b95439Data Mining; Fuzzy; Kernel Machines; Pattern Recognition; Soft Computing; Statistical Learning; algorithm;

SPECT

Lipo WangCarefully edited volume presenting the state of the art of Support Vector Machines.Presents theory, algorithms and applications.Includes numerous real-world applications, such as bioinformatics, text

disrupt

,Support Vector Machines – An Introduction,r machines (SVMs) a.k.a. kernel machines. The basic aim of this introduction. is to give, as far as possible, a condensed (but systematic) presentation of a novel learning paradigm embodied in SVMs. Our focus will be on the constructive learning algorithms for both the classification (pattern recogn

群居动物

Multiple Model Estimation for Nonlinear Classification, new formulation of the learning problem called Multiple Model Estimation. Whereas standard supervised-learning learning formulations (such as regression and classification) seek to describe a given (training) data set using a single (albeit complex) model, under multiple model formulation the goal