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AAAI
2011
13 years 9 months ago
Value Function Approximation in Reinforcement Learning Using the Fourier Basis
We describe the Fourier Basis, a linear value function approximation scheme based on the Fourier Series. We empirically evaluate its properties, and demonstrate that it performs w...
George Konidaris, Sarah Osentoski, Philip Thomas
CVPR
2010
IEEE
15 years 5 months ago
Learning Shift-Invariant Sparse Representation of Actions
A central problem in the analysis of motion capture (Mo- Cap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should fac...
Yi Li
FOGA
1990
14 years 10 months ago
A Hierarchical Approach to Learning the Boolean Multiplexer Function
This paper describes the recently developed genetic programming paradigm which genetically breeds populations of computer programs to solve problems. In genetic programming, the i...
John R. Koza
NIPS
1994
14 years 11 months ago
From Data Distributions to Regularization in Invariant Learning
Ideally pattern recognition machines provide constant output when the inputs are transformed under a group G of desired invariances. These invariances can be achieved by enhancing...
Todd K. Leen
IDEAL
2007
Springer
15 years 3 months ago
PINCoC : A Co-clustering Based Approach to Analyze Protein-Protein Interaction Networks
A novel technique to search for functional modules in a protein-protein interaction network is presented. The network is represented by the adjacency matrix associated with the und...
Clara Pizzuti, Simona E. Rombo