Read e-book online Advanced Data Mining and Applications: 6th International PDF

By Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing Cao, Jiang Zhong, Yong Feng (eds.)

ISBN-10: 3642173128

ISBN-13: 9783642173127

With the ever-growing strength of producing, transmitting, and gathering large quantities of knowledge, info overloadis nowan coming near near problemto mankind. the overpowering call for for info processing isn't just a few higher figuring out of information, but in addition a greater utilization of information promptly. facts mining, or wisdom discovery from databases, is proposed to achieve perception into elements ofdata and to assist peoplemakeinformed,sensible,and greater judgements. at the moment, becoming consciousness has been paid to the research, improvement, and alertness of knowledge mining. consequently there's an pressing desire for stylish suggestions and toolsthat can deal with new ?elds of knowledge mining, e. g. , spatialdata mining, biomedical information mining, and mining on high-speed and time-variant information streams. the information of knowledge mining also needs to be accelerated to new purposes. The sixth foreign convention on complex information Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the realm. It supplied a number one foreign discussion board for the dissemination of unique study leads to complicated information mining thoughts, functions, al- rithms, software program and platforms, and di?erent utilized disciplines. The convention attracted 361 on-line submissions from 34 di?erent international locations and components. All complete papers have been peer reviewed by means of a minimum of 3 contributors of this system Comm- tee composed of overseas specialists in facts mining ?elds. a complete variety of 118 papers have been permitted for the convention. among them, sixty three papers have been chosen as usual papers and fifty five papers have been chosen as brief papers.

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Additional info for Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II

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Its disadvantage lies in its dependency on the learning mechanism coupled with the inability to accurately derive the logic of the underlying decision surface [4] [5]. The pedagogical approach used considers the trained ANN or SVM as a black box and using the classifier algorithm as an oracle through which it tests its predicted responses [5]. While changing the input values, rules are extracted which express the 36 J. Guerreiro and D. Trigueiros relationship between inputs and outputs of the Neural Network or Support Vector Machine.

In: Proceedings of the 2005 International Joint Conference on Neural Networks (IJCNN 2005), Montreal, Canada, pp. 1205–1210 (2005) 10. : Negative correlation in incremental learning. Natural Computing 8, 280–320 (2009) 11. : Bagging Predictors. Machine Learning 24(2), 123–140 (1996) 12. : Boosting neural networks. Neural Computation 12, 1869–1887 (2000) 13. html (unpublished) 14. : Selective Negative Correlation Learning Approach to Incremental Learning. Neurocomputing 72(13-15), 2796–2805 (2009) 15.

LINUS [13] is a pioneer propositionalization method, which transforms clauses into propositional expressions as long as all body literals in the clauses are determined. Srinivasan and King [14] addressed the problem of non-determined literals in clauses by constructing boolean features from PROGOL clauses. Compared to the other ILP methods, the propositionalization methods incur high computational cost and heavy information loss in constructing binary features. RelAggs [15] tries to tackle the issues by considering aggregates in feature generation.

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Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II by Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing Cao, Jiang Zhong, Yong Feng (eds.)

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