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AAAI
2006
15 years 3 months ago
Efficient L1 Regularized Logistic Regression
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized...
Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. N...
COLT
2004
Springer
15 years 7 months ago
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary
We give an algorithm for the bandit version of a very general online optimization problem considered by Kalai and Vempala [1], for the case of an adaptive adversary. In this proble...
H. Brendan McMahan, Avrim Blum
ROBIO
2006
IEEE
129views Robotics» more  ROBIO 2006»
15 years 8 months ago
Learning Utility Surfaces for Movement Selection
— Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the ro...
Matthew Howard, Michael Gienger, Christian Goerick...
JMLR
2010
165views more  JMLR 2010»
14 years 8 months ago
Learning with Blocks: Composite Likelihood and Contrastive Divergence
Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper,...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
ICML
2008
IEEE
16 years 2 months ago
Modified MMI/MPE: a direct evaluation of the margin in speech recognition
In this paper we show how common speech recognition training criteria such as the Minimum Phone Error criterion or the Maximum Mutual Information criterion can be extended to inco...
Georg Heigold, Hermann Ney, Ralf Schlüter, Th...