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UAI
2003
15 years 4 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
117
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ICML
2004
IEEE
16 years 3 months ago
Learning large margin classifiers locally and globally
A new large margin classifier, named MaxiMin Margin Machine (M4 ) is proposed in this paper. This new classifier is constructed based on both a "local" and a "globa...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...
122
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ICML
2010
IEEE
15 years 3 months ago
Simple and Efficient Multiple Kernel Learning by Group Lasso
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In literature, MKL is often solved by an alternating approach: (1) the minimization of ...
Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Mic...
137
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NIPS
2004
15 years 4 months ago
Multiple Alignment of Continuous Time Series
Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained i...
Jennifer Listgarten, Radford M. Neal, Sam T. Rowei...
127
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CSL
2009
Springer
15 years 9 months ago
Model Checking FO(R) over One-Counter Processes and beyond
Abstract. One-counter processes are pushdown processes over a singleton stack alphabet (plus a stack-bottom symbol). We study the problems of model checking asynchronous products o...
Anthony Widjaja To