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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 10 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
ICML
2008
IEEE
14 years 8 months ago
Learning to classify with missing and corrupted features
After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularl...
Ofer Dekel, Ohad Shamir
ROBOCUP
2004
Springer
114views Robotics» more  ROBOCUP 2004»
14 years 1 months ago
Modular Learning System and Scheduling for Behavior Acquisition in Multi-agent Environment
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments such as RoboCup competitions since othe...
Yasutake Takahashi, Kazuhiro Edazawa, Minoru Asada
FLAIRS
2004
13 years 9 months ago
Cautious Cooperative Learning with Distributed Case-Based Reasoning
In this paper, we propose a cautious cooperative learning approach using distributed case-based reasoning. Our approach consists of two learning mechanisms: individual and coopera...
Leen-Kiat Soh
KDD
2012
ACM
207views Data Mining» more  KDD 2012»
11 years 10 months ago
Robust multi-task feature learning
Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning alg...
Pinghua Gong, Jieping Ye, Changshui Zhang