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SDM
2007
SIAM
104views Data Mining» more  SDM 2007»
15 years 4 months ago
Boosting Optimal Logical Patterns Using Noisy Data
We consider the supervised learning of a binary classifier from noisy observations. We use smooth boosting to linearly combine abstaining hypotheses, each of which maps a subcube...
Noam Goldberg, Chung-chieh Shan
PAKDD
2009
ACM
171views Data Mining» more  PAKDD 2009»
15 years 7 months ago
Detecting Abnormal Events via Hierarchical Dirichlet Processes
Abstract. Detecting abnormal event from video sequences is an important problem in computer vision and pattern recognition and a large number of algorithms have been devised to tac...
Xian-Xing Zhang, Hua Liu, Yang Gao, Derek Hao Hu
ICDM
2009
IEEE
160views Data Mining» more  ICDM 2009»
15 years 10 months ago
Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
Zhuang Wang, Slobodan Vucetic
AIED
2009
Springer
15 years 9 months ago
Revisiting Ill-Definedness and the Consequences for ITSs
: ITSs for ill-defined domains have attracted a lot of attention recently, which is well-deserved, as such ITSs are hard to develop. The first step towards such ITSs is reaching a ...
Antonija Mitrovic, Amali Weerasinghe
ECML
2007
Springer
15 years 9 months ago
Scale-Space Based Weak Regressors for Boosting
Boosting is a simple yet powerful modeling technique that is used in many machine learning and data mining related applications. In this paper, we propose a novel scale-space based...
Jin Hyeong Park, Chandan K. Reddy