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» Sentiment Mining Using Ensemble Classification Models
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ICDM
2005
IEEE
122views Data Mining» more  ICDM 2005»
14 years 2 months ago
Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation Trees
In practice, learning from data is often hampered by the limited training examples. In this paper, as the size of training data varies, we empirically investigate several probabil...
Kun Zhang, Zujia Xu, Jing Peng, Bill P. Buckles
BIBE
2008
IEEE
112views Bioinformatics» more  BIBE 2008»
13 years 11 months ago
Feature selection and classification for assessment of chronic stroke impairment
Recent advances of robotic/mechanical devices enable us to measure a subject's performance in an objective and precise manner. The main issue of using such devices is how to r...
Jae-Yoon Jung, Janice I. Glasgow, Stephen H. Scott
KDD
2004
ACM
181views Data Mining» more  KDD 2004»
14 years 9 months ago
Column-generation boosting methods for mixture of kernels
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single...
Jinbo Bi, Tong Zhang, Kristin P. Bennett
ICDM
2002
IEEE
70views Data Mining» more  ICDM 2002»
14 years 1 months ago
Progressive Modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and traini...
Wei Fan, Haixun Wang, Philip S. Yu, Shaw-hwa Lo, S...
VLDB
2008
ACM
147views Database» more  VLDB 2008»
14 years 9 months ago
Providing k-anonymity in data mining
In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in...
Arik Friedman, Ran Wolff, Assaf Schuster