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» Stable Computations with Gaussian Radial Basis Functions
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ICANN
2001
Springer
14 years 1 months ago
Incremental Support Vector Machine Learning: A Local Approach
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Liva Ralaivola, Florence d'Alché-Buc
DATAMINE
1998
145views more  DATAMINE 1998»
13 years 8 months ago
A Tutorial on Support Vector Machines for Pattern Recognition
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-...
Christopher J. C. Burges
NEUROSCIENCE
2001
Springer
14 years 29 days ago
Role of the Cerebellum in Time-Critical Goal-Oriented Behaviour: Anatomical Basis and Control Principle
The Brain is a slow computer yet humans can skillfully play games such as tennis where very fast reactions are required. Of particular interest is the evidence for strategic thinki...
Guido Bugmann
IJCAI
2001
13 years 10 months ago
Knowledge Extraction from Local Function Networks
Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a ten...
Kenneth McGarry, Stefan Wermter, John MacIntyre
TNN
2010
233views Management» more  TNN 2010»
13 years 3 months ago
A hierarchical RBF online learning algorithm for real-time 3-D scanner
In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding unit...
Stefano Ferrari, Francesco Bellocchio, Vincenzo Pi...