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ML
2002
ACM
145views Machine Learning» more  ML 2002»
15 years 3 days ago
Boosting Methods for Regression
In this paper we examine ensemble methods for regression that leverage or "boost" base regressors by iteratively calling them on modified samples. The most successful lev...
Nigel Duffy, David P. Helmbold
91
Voted
CORR
2010
Springer
92views Education» more  CORR 2010»
14 years 9 months ago
Regression on fixed-rank positive semidefinite matrices: a Riemannian approach
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space...
Gilles Meyer, Silvere Bonnabel, Rodolphe Sepulchre
JMLR
2010
189views more  JMLR 2010»
14 years 7 months ago
Adaptive Step-size Policy Gradients with Average Reward Metric
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
IR
2000
15 years 8 days ago
Automating the Construction of Internet Portals with Machine Learning
Domain-specific internet portals are growing in popularity because they gather content from the Web and organize it for easy access, retrieval and search. For example, www.campsear...
Andrew McCallum, Kamal Nigam, Jason Rennie, Kristi...
124
Voted
JMLR
2010
143views more  JMLR 2010»
14 years 7 months ago
Incremental Sigmoid Belief Networks for Grammar Learning
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
James Henderson, Ivan Titov