By Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
This booklet constitutes the refereed court cases of the eighth Pacific-Asia convention on wisdom Discovery and information mining, PAKDD 2004, beld in Sydney, Australia in could 2004.
The 50 revised complete papers and 31 revised brief papers offered have been conscientiously reviewed and chosen from a complete of 238 submissions. The papers are geared up in topical sections on category; clustering; organization ideas; novel algorithms; occasion mining, anomaly detection, and intrusion detection; ensemble studying; Bayesian community and graph mining; textual content mining; multimedia mining; textual content mining and net mining; statistical equipment, sequential info mining, and time sequence mining; and biomedical info mining.
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Extra resources for Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings
Edu/mccallum/bow/ http:/ /svmlight. joachims. 1 27 Datasets Reuters-21578: The Reuters-21578 Text Categorization Test Collection is a standard text categorization benchmark. We use the Mod-Apte split and evaluate all methods on the given train/test split with 135 classes. We also separately use random 70–30 train/test splits (averaged over 10 random splits), to test statistical significance, for a subset of 30 classes. We did feature selection by using stemming, stopword removal and only considered tokens which occurred in more than one document at least once, and selected the top 1000 features by mutual information.
Text categorization with support vector machines: learning with many relevant features. In Proceedings of ECML-1998. 2. R. E. Schapire and Y. Singer. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3): 135–168, 2000. 3. V. Vapnik. Statistical Learning Theory, John Wiley, 1998. 4. S. Sarawagi, S. Chakrabarti, and S. Godbole. Cross training: learning probabilistic mappings between topics. In Proceedings of the ACM SIGKDD-2003. 5. S. Godbole, S. Sarawagi, and S.
The composition of this positive set of related candidate classes is as yet unexplored. Secondly, we would like to theoretically understand the reasons for accuracy improvement in SVM-HF given that there is no extra information beyond terms and linear combinations of terms. Why should the learner pay attention to these features if all the information is already present in the pure text features? We would also like to explore using these methods in other application domains. Acknowledgments. The first author is supported by the Infosys Fellowship Award from Infosys Technologies Limited, Bangalore, India.