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» Optimized Approximation Algorithm in Neural Networks Without...
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NN
2002
Springer
136views Neural Networks» more  NN 2002»
14 years 9 months ago
Bayesian model search for mixture models based on optimizing variational bounds
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Zoubin Ghahramani
104
Voted
NPL
2006
172views more  NPL 2006»
14 years 9 months ago
Adapting RBF Neural Networks to Multi-Instance Learning
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training...
Min-Ling Zhang, Zhi-Hua Zhou
91
Voted
GECCO
2009
Springer
199views Optimization» more  GECCO 2009»
15 years 2 months ago
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with dece...
Jean-Baptiste Mouret, Stéphane Doncieux
91
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STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 1 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
IPMU
2010
Springer
15 years 1 months ago
Credal Sets Approximation by Lower Probabilities: Application to Credal Networks
Abstract. Credal sets are closed convex sets of probability mass functions. The lower probabilities specified by a credal set for each element of the power set can be used as cons...
Alessandro Antonucci, Fabio Cuzzolin