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» Predicting relative performance of classifiers from samples
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KDD
2001
ACM
163views Data Mining» more  KDD 2001»
14 years 8 months ago
Learning to recognize brain specific proteins based on low-level features from on-line prediction servers
During the last decade, the area of bioinformatics has produced an overwhelming amount of data, with the recently published draft of the human genome being the most prominent exam...
Henrik Boström, Joakim Cöster, Lars Aske...
ICPR
2004
IEEE
14 years 8 months ago
Selective Sampling Based on the Variation in Label Assignments
In this paper, a new selective sampling method for the active learning framework is presented. Initially, a small training set ? and a large unlabeled set ? are given. The goal is...
Piotr Juszczak, Robert P. W. Duin
TSMC
2008
172views more  TSMC 2008»
13 years 7 months ago
AdaBoost-Based Algorithm for Network Intrusion Detection
Abstract--Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security sy...
Weiming Hu, Wei Hu, Stephen J. Maybank
NETWORKING
2008
13 years 9 months ago
Network Performance Assessment Using Adaptive Traffic Sampling
Multimedia and real-time services are spreading all over the Internet. The delivery quality of such contents is closely related to its network performance, for example in terms suc...
René Serral-Gracià, Albert Cabellos-...
ECML
1991
Springer
13 years 11 months ago
Semi-Naive Bayesian Classifier
1 A novel semi-naive Bayesian classifier is introduced that is particularly suitable to data with many attributes. The naive Bayesian classifier is taken as a starting point and co...
Igor Kononenko