Boosting SVM classifiers by ensemble

14th International World Wide Web Conference, WWW2005

Volumn , Issue , 2005, Pages 1072-1073

  Dong, Yan Shi ^a   Han, Ke Song ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b MOTOROLA INC   (United States)

Author keywords

Classifier design and evaluation; Information filtering; Machine learning; Neural nets; Text processing

Indexed keywords

CLASSIFIER DESIGN AND EVALUATION; INFORMATION FILTERING; MACHINE-LEARNING; NEURAL NET; NEURAL NETS;

CLASSIFIERS; DECISION TREES; MACHINE DESIGN; SUPPORT VECTOR MACHINES; TEXT PROCESSING; THERMAL CONDUCTIVITY; WORD PROCESSING; WORLD WIDE WEB;

NEURAL NETWORKS;

EID: 32044463268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1062745.1062874     Document Type: Conference Paper

References (4)

1. Chang, Y. I. Boosting SVM classifiers with logistic regression. See www.stat.sinica.edu.tw/library/c-tec-rep/2003-03.pdf, 2003.
2. Dzeroski, S., and Zenko, B. Is combining classifiers better than selecting the best one? In Proc. 9th ICML, Morgan Kaufmann, San Francisco, CA, USA, 123-130, 2002.
3. Joachims, T. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 2002.
4. Yang, Y. A study on thresholding strategies for text categorization. In Proc. 24th ACM SIGIR, ACM Press, New York, NY, USA, 137-145, 2001. (Pubitemid 32951066)