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» Large Margin Classification Using the Perceptron Algorithm
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WWW
2008
ACM
14 years 10 months ago
Floatcascade learning for fast imbalanced web mining
This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applicatio...
Xiaoxun Zhang, Xueying Wang, Honglei Guo, Zhili Gu...
BMCBI
2004
208views more  BMCBI 2004»
13 years 9 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
BMCBI
2010
133views more  BMCBI 2010»
13 years 10 months ago
Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction
Background: With the rapid expansion of DNA sequencing databases, it is now feasible to identify relevant information from prior sequencing projects and completed genomes and appl...
Lance E. Palmer, Mathäus Dejori, Randall A. B...
JUCS
2007
95views more  JUCS 2007»
13 years 9 months ago
Using Place Invariants and Test Point Placement to Isolate Faults in Discrete Event Systems
: This paper describes a method of using Petri net P-invariants in system diagnosis. To model this process a net oriented fault classification is presented. Hence, the considered d...
Iwan Tabakow
ICML
2004
IEEE
14 years 10 months ago
Semi-supervised learning using randomized mincuts
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...