By Qiang Yang (auth.), Changjie Tang, Charles X. Ling, Xiaofang Zhou, Nick J. Cercone, Xue Li (eds.)
This e-book constitutes the refereed lawsuits of the 4th overseas convention on complex facts Mining and functions, ADMA 2008, held in Chengdu, China, in October 2008.
The 35 revised complete papers and forty three revised brief papers provided including the summary of two keynote lectures have been conscientiously reviewed and chosen from 304 submissions. The papers specialize in developments in info mining and peculiarities and demanding situations of actual international functions utilizing facts mining and have unique study ends up in facts mining, spanning functions, algorithms, software program and platforms, and various utilized disciplines with capability in information mining.
Read or Download Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings PDF
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Extra resources for Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
The experimental results show that our approach is more appropriate to this kind of problems than conventional classiﬁcation approaches. Keywords: Boosting, Acronym Extraction, Classiﬁcation. 1 Introduction One of the most basic assumptions of classiﬁcation is that all the instances are independently and identically distributed. Based on the assumption, instances are often equally treated as independent elements during training and testing. However, in many real-world applications, the instances are generated from differently distributed groups, and our target is to obtain the labels of instances from new groups.
To perform well on new, unseen examples, is of great importance for any domain, including natural language programming. While Bayesian graphical models were known for being a powerful mechanism for knowledge representation and reasoning under conditions of uncertainty, is was only after the introduction of the so-called Naïve Bayesian classifier (,) that they were regarded as classifiers, with a prediction performance similar to state-of-the-art classifiers. The Naïve Bayesian classifier performs inference by applying Bayes rule to compute the posterior probability of a class C, given a particular vector of input variables Ai.
Group outputs a hypothesis f through weighted majority vote of the T ‘weak’ hypotheses. Groupdisc ). 32 W. Ni et al. Groupdisc follows the framework of AdaBoost, there are considerable diﬀerences between them. First, the weighting scheme are conducted on groups rather than on instances as in AdaBoost; Second, the ‘goodness’ of weak hypotheses is evaluated on group level rather than on instance level as in AdaBoost; Third, the prediction of hypothesis takes values in vector space rather than in discrete label space as in AdaBoost.
Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings by Qiang Yang (auth.), Changjie Tang, Charles X. Ling, Xiaofang Zhou, Nick J. Cercone, Xue Li (eds.)