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2010
Tsinghua U.
14 years 6 months ago
Distribution-Specific Agnostic Boosting
We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for t...
Vitaly Feldman
NIPS
1993
13 years 10 months ago
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communicati...
Justin A. Boyan, Michael L. Littman
DAWAK
2006
Springer
14 years 9 days ago
Learning Classifiers from Distributed, Ontology-Extended Data Sources
Abstract. There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data source...
Doina Caragea, Jun Zhang 0002, Jyotishman Pathak, ...
ARTMED
1999
92views more  ARTMED 1999»
13 years 8 months ago
Two-Stage Machine Learning model for guideline development
We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia staging. Dementia staging in clin...
Subramani Mani, William Rodman Shankle, Malcolm B....
ICTAI
2002
IEEE
14 years 1 months ago
Machine Learning for Software Engineering: Case Studies in Software Reuse
There are many machine learning algorithms currently available. In the 21st century, the problem no longer lies in writing the learner, but in choosing which learners to run on a ...
Justin S. Di Stefano, Tim Menzies