Titlebook: Robust Data Mining; Petros Xanthopoulos,Panos M. Pardalos,Theodore B. Book 2013 Petros Xanthopoulos,Panos M. Pardalos,Theodore B. Trafali

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书目名称Robust Data Mining影响因子(影响力)




书目名称Robust Data Mining影响因子(影响力)学科排名




书目名称Robust Data Mining网络公开度




书目名称Robust Data Mining网络公开度学科排名




书目名称Robust Data Mining被引频次




书目名称Robust Data Mining被引频次学科排名




书目名称Robust Data Mining年度引用




书目名称Robust Data Mining年度引用学科排名




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书目名称Robust Data Mining读者反馈学科排名

令人悲伤

Introduction,or among all these is their ability to extract useful patterns and associations from data usually stored in large databases. Thus DM techniques aim to provide knowledge and interesting interpretation of, usually, vast amounts of data. This task is crucial, especially today, mainly because of the eme

legislate

Principal Component Analysis,ds and simple patterns in a group of samples. It has application in several areas of engineering. It is popular from computational perspective as it requires only an eigendecomposition or singular value decomposition. There are two alternative optimization approaches for obtaining principal componen

牵索

Linear Discriminant Analysis,s (LDA) was proposed by R. Fischer in 1936. It consists in finding the projection hyperplane that minimizes the interclass variance and maximizes the distance between the projected means of the classes. Similarly to PCA, these two objectives can be solved by solving an eigenvalue problem with the co

人类学家

Support Vector Machines,eptually simplest algorithms whereas at the same time one of the best especially for binary classification. Here we illustrate the mathematical formulation of SVM together with its robust equivalent for the most common uncertainty sets.

含铁

CHURL

Anthropoid

Principal Component Analysis,d up providing the same solution. It is necessary to study and understand both of these alternative approaches. In the second part of this chapter we present the robust counterpart formulation of PCA and demonstrate how such a formulation can be used in practice in order to produce sparse solutions.

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FRAUD

Conclusion, binary rule of the type “if feature . is more than .. and feature . less than .. then the sample belongs to class ..” There has been significant amount of research in the area of prior knowledge classification [33, 49] but there has not been a significant study of robust optimization on this direction.