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» Results of the KDD'99 Classifier Learning
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BMCBI
2006
173views more  BMCBI 2006»
13 years 9 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen
IAT
2007
IEEE
14 years 3 months ago
Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods
Phishing attack is a kind of identity theft which tries to steal confidential data like on-line bank account information. In a phishing attack scenario, attacker deceives users by...
Alireza Saberi, Mojtaba Vahidi, Behrouz Minaei-Bid...
ICML
2001
IEEE
14 years 9 months ago
Round Robin Rule Learning
In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is kno...
Johannes Fürnkranz
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 9 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
AVBPA
2003
Springer
133views Biometrics» more  AVBPA 2003»
14 years 20 days ago
LUT-Based Adaboost for Gender Classification
There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but ar...
Bo Wu, Haizhou Ai, Chang Huang