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» Data Mining for Genetics: A Genetic Algorithm Approach
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ICDM
2002
IEEE
133views Data Mining» more  ICDM 2002»
15 years 8 months ago
Learning with Progressive Transductive Support Vector Machine
Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead ...
Yisong Chen, Guoping Wang, Shihai Dong
120
Voted
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
15 years 7 months ago
OPTICS-OF: Identifying Local Outliers
: For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commer...
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. ...
150
Voted
ESWA
2006
148views more  ESWA 2006»
15 years 3 months ago
Applications of artificial intelligence in bioinformatics: A review
Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology. With the availability of different types of AI algor...
Zoheir Ezziane
116
Voted
ICDM
2006
IEEE
76views Data Mining» more  ICDM 2006»
15 years 9 months ago
How Bayesians Debug
Manual debugging is expensive. And the high cost has motivated extensive research on automated fault localization in both software engineering and data mining communities. Fault l...
Chao Liu 0001, Zeng Lian, Jiawei Han
124
Voted
PKDD
2005
Springer
153views Data Mining» more  PKDD 2005»
15 years 9 months ago
A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston
Abstract. Several new miners for frequent subgraphs have been published recently. Whereas new approaches are presented in detail, the quantitative evaluations are often of limited ...
Marc Wörlein, Thorsten Meinl, Ingrid Fischer,...