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EUROCRYPT
2001
Springer
15 years 2 months ago
Hyper-bent Functions
Bent functions have maximal minimum distance to the set of affine functions. In other words, they achieve the maximal minimum distance to all the coordinate functions of affine mon...
Amr M. Youssef, Guang Gong
RSFDGRC
2005
Springer
126views Data Mining» more  RSFDGRC 2005»
15 years 3 months ago
Rough Sets and Higher Order Vagueness
Abstract. We present a rough set approach to vague concept approximation within the adaptive learning framework. In particular, the role of extensions of approximation spaces in se...
Andrzej Skowron, Roman W. Swiniarski
NIPS
1996
14 years 11 months ago
Radial Basis Function Networks and Complexity Regularization in Function Learning
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
Adam Krzyzak, Tamás Linder
ESANN
2008
14 years 11 months ago
Multilayer Perceptrons with Radial Basis Functions as Value Functions in Reinforcement Learning
Using multilayer perceptrons (MLPs) to approximate the state-action value function in reinforcement learning (RL) algorithms could become a nightmare due to the constant possibilit...
Victor Uc Cetina
BMCBI
2010
179views more  BMCBI 2010»
14 years 10 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...