By Qiang Yang (auth.), Changjie Tang, Charles X. Ling, Xiaofang Zhou, Nick J. Cercone, Xue Li (eds.)
This publication constitutes the refereed court cases of the 4th overseas convention on complicated facts Mining and functions, ADMA 2008, held in Chengdu, China, in October 2008.
The 35 revised complete papers and forty three revised brief papers awarded including the summary of two keynote lectures have been conscientiously reviewed and chosen from 304 submissions. The papers concentrate on developments in facts mining and peculiarities and demanding situations of genuine international functions utilizing information mining and have unique examine ends up in information mining, spanning purposes, algorithms, software program and platforms, and diversified utilized disciplines with strength in info mining.
Read Online or Download Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings PDF
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Additional info for Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
2 Boosting Boosting is one of most popular classiﬁcation algorithms for its eﬀectivity and simplicity. The basic idea of boosting is to obtain an accurate hypothesis by combining many less accurate ‘weak’ hypotheses. Freund et al.  proposed the ﬁrst well-known boosting algorithm AdaBoost in which the weighting scheme of ‘weak’ hypotheses is adaptive to their performance. Later, Schapire et al.  proposed an improved AdaBoost algorithm in which the hypotheses can give conﬁdences to their predictions.
However, the requirement is much stronger in our algorithm because the predictions of weak hypotheses are vectors. The weak hypotheses should be carefully designed to meet the requirement. In this paper, we make use of the conventional AdaBoost with 30 rounds as the weak hypothesis. The choice is based on several considerations: First, AdaBoost performs well on instance level classiﬁcation problems and the performance is also desirable when evaluated on group level. Second, AdaBoost can be adapted to weighted training set easily.
Upon completion of the annotation phase, Bayesian networks were utilized in order to infer on the most probable programming interpretation, given the input of the user. The whole architecture, as well as the linguistic tools that were used, shall be discussed in the next section. 1 Background The first attempts in natural language programming were rather ambitious, targeting the generation of complete computer programs that would compile and run. As an example, the “NLC” prototype  aimed at creating a natural language interface for processing data stored in arrays and matrices, with the ability of handling low level operations such as the transformation of numbers into type declarations.