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» Learning to Classify Texts Using Positive and Unlabeled Data
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ECCV
2006
Springer
13 years 9 months ago
Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost
Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of joint positions or pose angles. Such recognition is d...
Fengjun Lv, Ramakant Nevatia
WWW
2010
ACM
14 years 2 months ago
Large-scale bot detection for search engines
In this paper, we propose a semi-supervised learning approach for classifying program (bot) generated web search traffic from that of genuine human users. The work is motivated by...
Hongwen Kang, Kuansan Wang, David Soukal, Fritz Be...
BMCBI
2006
134views more  BMCBI 2006»
13 years 7 months ago
Application of machine learning in SNP discovery
Background: Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given ...
Lakshmi K. Matukumalli, John J. Grefenstette, Davi...
KDD
2002
ACM
108views Data Mining» more  KDD 2002»
14 years 7 months ago
Incremental Machine Learning to Reduce Biochemistry Lab Costs in the Search for Drug Discovery
This paper promotes the use of supervised machine learning in laboratory settings where chemists have a large number of samples to test for some property, and are interested in id...
George Forman
IPM
2002
106views more  IPM 2002»
13 years 7 months ago
A feature mining based approach for the classification of text documents into disjoint classes
This paper proposes a new approach for classifying text documents into two disjoint classes. The new approach is based on extracting patterns, in the form of two logical expressio...
Salvador Nieto Sánchez, Evangelos Triantaph...