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IJCAI
1997
15 years 8 months ago
Extracting Propositions from Trained Neural Networks
This paper presents an algorithm for extract­ ing propositions from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural networ...
Hiroshi Tsukimoto
OL
2007
156views more  OL 2007»
15 years 6 months ago
A trust region SQP algorithm for mixed-integer nonlinear programming
We propose a modified sequential quadratic programming (SQP) method for solving mixed-integer nonlinear programming problems. Under the assumption that integer variables have a s...
Oliver Exler, Klaus Schittkowski
212
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CORR
2012
Springer
196views Education» more  CORR 2012»
14 years 2 months ago
PAC-Bayesian Policy Evaluation for Reinforcement Learning
Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, lar...
Mahdi Milani Fard, Joelle Pineau, Csaba Szepesv&aa...
259
Voted
TSP
2012
14 years 2 months ago
Optimized Compact-Support Interpolation Kernels
Abstract—In this paper, we investigate the problem of designing compact-support interpolation kernels for a given class of signals. By using calculus of variations, we simplify t...
Ramtin Madani, Ali Ayremlou, Arash Amini, Farrokh ...
158
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COLT
1993
Springer
15 years 11 months ago
Learning from a Population of Hypotheses
We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approxima...
Michael J. Kearns, H. Sebastian Seung