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105
Voted
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 3 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
NIPS
2004
15 years 1 months ago
The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees
Prediction suffix trees (PST) provide a popular and effective tool for tasks such as compression, classification, and language modeling. In this paper we take a decision theoretic...
Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
DAGSTUHL
2009
15 years 22 days ago
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Abstract. In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call su...
Erzsébet Merényi, Kadim Tasdemir, Li...
89
Voted
CP
2004
Springer
15 years 5 months ago
Leveraging the Learning Power of Examples in Automated Constraint Acquisition
Constraint programming is rapidly becoming the technology of choice for modeling and solving complex combinatorial problems. However, users of constraint programming technology nee...
Christian Bessière, Remi Coletta, Eugene C....
93
Voted
EWRL
2008
15 years 1 months ago
Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem
Two variable metric reinforcement learning methods, the natural actor-critic algorithm and the covariance matrix adaptation evolution strategy, are compared on a conceptual level a...
Verena Heidrich-Meisner, Christian Igel