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» Machine Learning by Function Decomposition
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ICML
2007
IEEE
16 years 17 days ago
Constructing basis functions from directed graphs for value function approximation
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
Jeffrey Johns, Sridhar Mahadevan
ICML
2005
IEEE
16 years 17 days ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
PLDI
1993
ACM
15 years 3 months ago
Global Optimizations for Parallelism and Locality on Scalable Parallel Machines
Data locality is critical to achievinghigh performance on large-scale parallel machines. Non-local data accesses result in communication that can greatly impact performance. Thus ...
Jennifer-Ann M. Anderson, Monica S. Lam
ML
2010
ACM
181views Machine Learning» more  ML 2010»
14 years 10 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
ICML
2000
IEEE
16 years 17 days ago
Behavioral Cloning of Student Pilots with Modular Neural Networks
This paper investigates how behavioral cloning can be used to decrease training time for students learning to y on simulators. The challenges presented to each student must be tai...
Charles W. Anderson, Bruce A. Draper, David A. Pet...