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ICANN
2009
Springer
15 years 9 months ago
Constrained Learning Vector Quantization or Relaxed k-Separability
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Marek Grochowski, Wlodzislaw Duch
AIIA
2001
Springer
15 years 9 months ago
Wide Coverage Incremental Parsing by Learning Attachment Preferences
This paper presents a novel method for wide coverage parsing using an incremental strategy, which is psycholinguistically motivated. A recursive neural network is trained on treeba...
Fabrizio Costa, Vincenzo Lombardo, Paolo Frasconi,...
ICANN
2001
Springer
15 years 9 months ago
Fast Training of Support Vector Machines by Extracting Boundary Data
Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification p...
Shigeo Abe, Takuya Inoue
ICANN
2001
Springer
15 years 9 months ago
Feature Extraction Using ICA
In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving ...
Nojun Kwak, Chong-Ho Choi, Jin-Young Choi
ICANN
2001
Springer
15 years 9 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