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» Temporal Data Classification Using Linear Classifiers
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ECCV
2006
Springer
15 years 5 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
CATE
2004
190views Education» more  CATE 2004»
15 years 4 months ago
Enhancing Online Learning Performance: An Application of Data Mining Methods
Recently web-based educational systems collect vast amounts of data on user patterns, and data mining methods can be applied to these databases to discover interesting associations...
Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F...
BMCBI
2006
129views more  BMCBI 2006»
15 years 3 months ago
Identifying genes that contribute most to good classification in microarrays
Background: The goal of most microarray studies is either the identification of genes that are most differentially expressed or the creation of a good classification rule. The dis...
Stuart G. Baker, Barnett S. Kramer
BMCBI
2008
116views more  BMCBI 2008»
15 years 3 months ago
The combination approach of SVM and ECOC for powerful identification and classification of transcription factor
Background: Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network co...
Guangyong Zheng, Ziliang Qian, Qing Yang, Chaochun...
PRIS
2008
15 years 4 months ago
Comparison of Adaboost and ADTboost for Feature Subset Selection
Abstract. This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting ...
Martin Drauschke, Wolfgang Förstner