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KDD
2009
ACM
150views Data Mining» more  KDD 2009»
16 years 6 months ago
Information theoretic regularization for semi-supervised boosting
We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...
Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
CA
2003
IEEE
15 years 11 months ago
Expressive Gesture Animation Based on Non Parametric Learning of Sensory-Motor Models
This paper presents an efficient method of learning motion control for autonomous animated characters. The method uses a non parametric learning approach which identifies non line...
Sylvie Gibet, Pierre-Francois Marteau
NIPS
2008
15 years 7 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
SAS
2001
Springer
15 years 10 months ago
Solving Regular Tree Grammar Based Constraints
This paper describes the precise speci cation, design, analysis, implementation, and measurements of an e cient algorithm for solving regular tree grammar based constraints. The p...
Yanhong A. Liu, Ning Li, Scott D. Stoller
ICML
2007
IEEE
16 years 6 months ago
Scalable training of L1-regularized log-linear models
The l-bfgs limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be us...
Galen Andrew, Jianfeng Gao