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» An algorithmic approach to knowledge evolution
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GECCO
2009
Springer
141views Optimization» more  GECCO 2009»
16 years 1 months ago
Distributed hyper-heuristics for real parameter optimization
Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance tha...
Marco Biazzini, Balázs Bánhelyi, Alb...
SAC
2004
ACM
15 years 12 months ago
Unsupervised learning techniques for an intrusion detection system
With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are incr...
Stefano Zanero, Sergio M. Savaresi
IWANN
2001
Springer
15 years 11 months ago
Evolving RBF Neural Networks
This paper is focused on determining the parameters of radial basis function neural networks (number of neurons, and their respective centers and radii) automatically. While this ...
Víctor Manuel Rivas Santos, Pedro A. Castil...
TNN
1998
111views more  TNN 1998»
15 years 6 months ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
CLEIEJ
2008
82views more  CLEIEJ 2008»
15 years 6 months ago
Postal Envelope Segmentation using Learning-Based Approach
This paper presents a learning-based approach to segment postal address blocks where the learning step uses only one pair of images (a sample image and its ideal segmented solutio...
Horacio Andrés Legal-Ayala, Jacques Facon, ...