NDLI: Research on attribute interval optimization method for segmentation based SVM and the Decision Tree Learning

Content Provider	IEEE Xplore Digital Library
Author	Huang Hua Zhang Dexian
Copyright Year	2010
Description	Author affiliation: College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China (Huang Hua; Zhang Dexian)
Abstract	In recent years, statistical learning theory ( for short SLT)and support vector machine(for short SVM) has become an international field of machine learning new hotspot. Decision tree classification learning algorithm is one of the most widely used and very practical inductive reasoning method, In machine learning, data mining, signal processing, intelligent control, artificial intelligence area has a very important theoretical meaning and practical value. It has been successfully applied Wide range of fields from learning medical diagnosis to assessment the credit risk of learning loan applications. Based on Decision Tree Learning and SVM has obvious advantages in their respective, the integration of decision tree and SVM learning will solve the problems that exist between the two effective ways to improve learning ability and decision tree classification performance, give full play to the advantages of both to make up for both insufficient to resolve the two difficult to solve long-standing problems. This paper based on support vector machine classification surface model and support vector point distribution, with the decision tree Classification surface and support vector machine Classification surface effective approximation as the basic starting point, explore new ways on the fusion of decision tree and SVM. Focused study based on support vector points and categories surface shape characteristics' attribute interval optimization method for segmentation in order to achieve the performance of decision tree optimization.
File Size	581761
File Format	PDF
ISBN	9781424463671
e-ISBN	9781424463701
DOI	10.1109/ICETC.2010.5529640
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Statistical learning optimization classification surface Optimization methods fusing leaning Classification algorithms SVM Support vector machines Support vector machine classification Signal processing algorithms attribute interval partitioning optimization Machine learning Decision trees decision tree leaning Classification tree analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in