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» Training Methods for Adaptive Boosting of Neural Networks
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ICTAI
2007
IEEE
15 years 4 months ago
An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...
NPL
1998
129views more  NPL 1998»
14 years 9 months ago
Extraction of Logical Rules from Neural Networks
A new architecture and method for feature selection and extraction of logical rules from neural networks trained with backpropagation algorithm is presented. The network consists ...
Wlodzislaw Duch, Rafal Adamczak, Krzysztof Grabcze...
IDA
2002
Springer
14 years 9 months ago
Boosting strategy for classification
This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifi...
Huma Lodhi, Grigoris J. Karakoulas, John Shawe-Tay...
ECAI
1998
Springer
15 years 1 months ago
The Neural Path to Dialogue Acts
This paper presents a neural network approach to the problem of nding the dialogue act for a given utterance. So far only symbolic, decision tree and statistical approaches were ut...
M. Kipp
ESANN
2004
14 years 11 months ago
Online policy adaptation for ensemble classifiers
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of usin...
Christos Dimitrakakis, Samy Bengio