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» Training Methods for Adaptive Boosting of Neural Networks
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AIIA
2001
Springer
15 years 2 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,...
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ICANN
2001
Springer
15 years 2 months ago
Sparse Kernel Regressors
Sparse kernel regressors have become popular by applying the support vector method to regression problems. Although this approach has been shown to exhibit excellent generalization...
Volker Roth
ICANN
2007
Springer
15 years 1 months ago
Resilient Approximation of Kernel Classifiers
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
Thorsten Suttorp, Christian Igel
ICANN
2007
Springer
15 years 1 months ago
Active Learning to Support the Generation of Meta-examples
Meta-Learning has been used to select algorithms based on the features of the problems being tackled. Each training example in this context, i.e. each meta-example, stores the feat...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...
NN
2006
Springer
14 years 9 months ago
Machine learning in sedimentation modelling
The paper presents machine learning (ML) models that predict sedimentation in the harbour basin of the Port of Rotterdam. The important factors affecting the sedimentation process...
Biswanath Bhattacharya, Dimitri P. Solomatine