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KDD
1995
ACM
148views Data Mining» more  KDD 1995»
13 years 11 months ago
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning
Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this p...
Philip K. Chan, Salvatore J. Stolfo
ICDM
2009
IEEE
151views Data Mining» more  ICDM 2009»
13 years 5 months ago
TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents
The amount of text data on the Internet is growing at a very fast rate. Online text repositories for news agencies, digital libraries and other organizations currently store gigaan...
Haimonti Dutta, Xianshu Zhu, Tushar Mahule, Hillol...
PKDD
2004
Springer
155views Data Mining» more  PKDD 2004»
14 years 25 days ago
Ensemble Feature Ranking
A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Sele...
Kees Jong, Jérémie Mary, Antoine Cor...
PKDD
1999
Springer
90views Data Mining» more  PKDD 1999»
13 years 11 months ago
Learning from Highly Structured Data by Decomposition
This paper addresses the problem of learning from highly structured data. Speci cally, it describes a procedure, called decomposition, that allows a learner to access automatically...
René MacKinney-Romero, Christophe G. Giraud...
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
13 years 8 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava