152 search results - page 4 / 31 » Training neural networks with noisy data as an ill-posed pro... |
IJCNN
2000
IEEE
13 years 11 months ago
2000
IEEE
A training data selection method for multi-class data is proposed. This method can be used for multilayer neural networks (MLNN). The MLNN can be applied to pattern classification...
IJCAI
1997
13 years 8 months ago
1997
This paper presents an algorithm for extract ing propositions from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural networ...
MICAI
2010
Springer
2010
Springer
Combining Neural Networks Based on Dempster-Shafer Theory for Classifying Data with Imperfect Labels
13 years 5 months ago
This paper addresses the supervised learning in which the class membership of training data are subject to uncertainty. This problem is tackled in the framework of the Dempster-Sha...
ICANN
2007
Springer
13 years 11 months ago
2007
Springer
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...
COLT
1995
Springer
13 years 11 months ago
1995
Springer
The Noise Sensitivity Signature (NSS), originally introduced by Grossman and Lapedes (1993), was proposed as an alternative to cross validation for selecting network complexity. I...