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KDD
2010
ACM
274views Data Mining» more  KDD 2010»
13 years 9 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
ADMA
2006
Springer
153views Data Mining» more  ADMA 2006»
13 years 7 months ago
An Effective Combination Based on Class-Wise Expertise of Diverse Classifiers for Predictive Toxicology Data Mining
This paper presents a study on the combination of different classifiers for toxicity prediction. Two combination operators for the Multiple-Classifier System definition are also pr...
Daniel Neagu, Gongde Guo, Shanshan Wang
PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
13 years 4 months ago
Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Zhao Xu, Kristian Kersting, Thorsten Joachims
KDD
2003
ACM
243views Data Mining» more  KDD 2003»
14 years 6 months ago
Accurate decision trees for mining high-speed data streams
In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-ti...
João Gama, Pedro Medas, Ricardo Rocha
CIS
2004
Springer
13 years 11 months ago
Knowledge Maintenance on Data Streams with Concept Drifting
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
Juggapong Natwichai, Xue Li