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Evolutionary granular kernel machines.

Jin, Bo.

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Evolutionary granular kernel machines.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Evolutionary granular kernel machines./
Author:	Jin, Bo.
Description:	120 p.
Notes:	Adviser: Yan-Qing Zhang.
Contained By:	Dissertation Abstracts International68-04B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3260562

Evolutionary granular kernel machines.
Jin, Bo.

Evolutionary granular kernel machines. - 120 p.

Adviser: Yan-Qing Zhang.

Thesis (Ph.D.)--Georgia State University, 2007.

Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research.Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Evolutionary granular kernel machines.
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035 $a (UMI)AAI3260562
035 $a AAI3260562
040 $a UMI $c UMI
100 1 $a Jin, Bo. $3 1279430
245 1 0 $a Evolutionary granular kernel machines.
300 $a 120 p.
500 $a Adviser: Yan-Qing Zhang.
500 $a Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2446.
502 $a Thesis (Ph.D.)--Georgia State University, 2007.
520 $a Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research.
520 $a For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently.
520 $a Index words. Bioinformatics, Computational Intelligence, Data Mining, Evolutionary Granular Kernel Machines, Evolutionary Granular Kernel Trees, Genetic Algorithms, Granular Computing, Granular Kernel Tree Structure Evolving System, Machine Learning, Minimum Enclosing Ball, Statistical Learning, Support Vector Machines
590 $a School code: 0079.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a Georgia State University. $3 1018518
773 0 $t Dissertation Abstracts International $g 68-04B.
790 $a 0079
790 1 0 $a Zhang, Yan-Qing, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3260562