ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the ca...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We address the problem of evaluating the risk of a given model accurately at minimal labeling costs. This problem occurs in situations in which risk estimates cannot be obtained f...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Abstract--Reinforcement learning (RL) research typically develops algorithms for helping an RL agent best achieve its goals-however they came to be defined--while ignoring the rela...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the m...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
This paper experimentally evaluates multiagent learning algorithms playing repeated matrix games to maximize their cumulative return. Previous works assessed that Qlearning surpas...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likeliho...