Transformation and weighting in regression
Transformation and weighting in regression
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using urls and table layout for web classification tasks
Proceedings of the 13th international conference on World Wide Web
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
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Web page classification is facing great challenges since there is a huge repository and diversity of information. As known, each web page varies both in content and quality, just as PageRank suggested. Typical machine learning algorithms take advantage of positive and negative examples to train a classifier; however, it has been neglected that each instance has a different weight, which can be user pre-defined. This paper presents an effective algorithm based on Cost-Sensitive Support Vector Machine (CS-SVM) to improve the accuracy of classification. During the training process of CS-SVM, different cost factors are attached on the training errors to generate an optimized hyperplane. Our experiments show that CS-SVM outperforms SVM on the standard ODP data set. The web pages with relative high PageRank values contribute most to the classifier and using them for training can exceed the random sampling technique.