Download Advances in Knowledge Discovery and Data Mining: 8th by Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang PDF

By Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang

This ebook constitutes the refereed lawsuits of the eighth Pacific-Asia convention on wisdom Discovery and information mining, PAKDD 2004, beld in Sydney, Australia in may well 2004.

The 50 revised complete papers and 31 revised brief papers provided have been conscientiously reviewed and chosen from a complete of 238 submissions. The papers are equipped in topical sections on class; 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 facts mining.

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For ease of exposition, we henceforth assume that the data space of the data set D has been partitioned into disjoint equal volume grid cells by using partition. Definition 2. The neighboring cells of the cell B are the cells that are adjacent to B. The cell neighbors with if is one of the neighbor cells of The set of neighbor cells of B contains two classes: (1) those have a common boundary side with B and (2) those are adjacent with B on single point. Each grid cell has neighbor cells except for the cells of boundary of the data space.

In the first iteration, train the basic SVM ensemble. 2. For each SVM trained, remove those negative training instances which are within a threshold distance (band) from the learnt hyperplane. Re-train the ensemble. S. Godbole and S. Sarawagi 26 We call this method the band-removal method (denoted BandSVM). We use a heldout validation dataset to choose the band size. An appropriate band-size tries to achieve the fine balance between large-margin separation, achieved by removing highly related points, and over-generalization, achieved by removing points truly belonging to the negative class.

Proc 16th Australian Joint Conference on Artificial Intelligence. Springer-Verlag, 2003. 4. L. J. Merz. UCI Repository of machine learning databases. Irvine, CA, 1998. html 5. T. G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10(7) 1895–1924, 1998 6. C. Nadeau and Y. Bengio. Inference for the generalization error. In Machine Learning 52:239– 281, 2003 7. R. Quinlan. 5: Programs for Machine Learning, Morgan Kaufmann, 1993.

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