Titlebook: Soft Computing for Knowledge Discovery and Data Mining; Oded Maimon,Lior Rokach Book 2008 Springer-Verlag US 2008 Bayesian network.Cluster

Autonomous

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書目名稱Soft Computing for Knowledge Discovery and Data Mining網(wǎng)絡公開度學科排名




書目名稱Soft Computing for Knowledge Discovery and Data Mining被引頻次




書目名稱Soft Computing for Knowledge Discovery and Data Mining被引頻次學科排名




書目名稱Soft Computing for Knowledge Discovery and Data Mining年度引用




書目名稱Soft Computing for Knowledge Discovery and Data Mining年度引用學科排名




書目名稱Soft Computing for Knowledge Discovery and Data Mining讀者反饋




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Catheter

Oded Maimon,Lior RokachIllustrations of various applications including manufacturing, medical, banking, insurance and others.Includes various real-world case studies with detailed results.Edited by the highly-regarded edito

OATH

es with detailed results.Edited by the highly-regarded edito.Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability...

Migratory

偏離

Swarm Intelligence Algorithms for Data Clusteringinally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.

內向者

Book 2008nt because it enables modeling and knowledge extraction from abundant data availability...Soft Computing for Knowledge Discovery and Data Mining. introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approach

松緊帶

Support Vector Machines and Fuzzy Systems More importantly, an SVM can work very well on a high (or even infinite) dimensional feature space. This chapter presents a survey of the connection between fuzzy classifiers and kernel machines. A significant portion of the chapter is built upon material from articles we have written, in particular (Chen and Wang, 2003a, Chen and Wang, 2003b).