By Qiang Yang (auth.), Changjie Tang, Charles X. Ling, Xiaofang Zhou, Nick J. Cercone, Xue Li (eds.)
This booklet constitutes the refereed lawsuits of the 4th overseas convention on complex information Mining and purposes, 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 specialise in developments in information mining and peculiarities and demanding situations of actual global purposes utilizing facts mining and have unique study leads to info mining, spanning purposes, algorithms, software program and platforms, and diversified utilized disciplines with capability in info 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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Additional info for Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
Initialize: weights of each group w1i = 1. For t = 1, . . , T – Set the weight distribution pt of groups in S: pit = Èw i t n i=1 wti – Train weak hypothesis ht : X → Y on S with distribution pt – Calculate the weighted training error of ht : n pit ½[Yi =f (Xi )] εt = i=1 εt 1−εt – Choose βt = – Update the weights 1−½[Y =f (X )] i i i = wti βt wt+1 Output: the ﬁnal hypothesis T fT (X) = max Y ∈Y (log t=1 1 )½[ht (X)=Y ] βt Fig. 2. Group Algorithm only small number of groups. As in acronym extraction, only small number of sentences contain genuine acronym-expansion pairs and the acronyms appear once in most of them.
Group. 1 Related Work Automatic Acronym-Expansion Extraction The research on automatic acronym-expansion extraction is mainly focused on recognizing the genuine acronym-expansion pair from a given sentence. The Boosting over Groups and Its Application to Acronym-Expansion Extraction 29 f (x)=w·x + b Peer to peer, referred to as P2P, is a type of ad-hoc computer network. 6, … …… candidate pairs feature vectors
Groupreal could converge to a bit lower bound that AdaBoost. Groupdisc , which gives an experimental evidence that the classiﬁcation conﬁdence deﬁned in Sect. 3 is helpful to guide the training process. 04 0 10 20 30 40 50 60 70 80 90 100 number of iterations Fig. 6. Group on training set 5 Conclusion and Future Work In this paper, we have addressed a new kind of classiﬁcation problem where instances can be naturally grouped. The kind of problem has its own uniqueness and is widely existed in real-world applications.