ICMLC
2010
Springer
13 years 9 months ago
2010
Springer
: Bagging (Bootstrap Aggregating) has been proved to be a useful, effective and simple ensemble learning methodology. In generic bagging methods, all the classifiers which are trai...
MICAI
2010
Springer
2010
Springer
Combining Neural Networks Based on Dempster-Shafer Theory for Classifying Data with Imperfect Labels
13 years 10 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...
MCS
2002
Springer
13 years 11 months ago
2002
Springer
In classifier combining, one tries to fuse the information that is given by a set of base classifiers. In such a process, one of the difficulties is how to deal with the variabilit...
PRL
2008
13 years 11 months ago
2008
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the comb...
PR
2008
13 years 11 months ago
2008
This paper presents a strategy to improve the AdaBoost algorithm with a quadratic combination of base classifiers. We observe that learning this combination is necessary to get be...
DMIN
2006
14 years 25 days ago
2006
- An ideal ensemble is composed of base classifiers that perform well and that have minimal overlap in their errors. Eliminating classifiers from an ensemble based on a criterion t...
ESANN
2004
14 years 25 days ago
2004
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...
DIS
2008
Springer
14 years 1 months ago
2008
Springer
We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
MCS
2010
Springer
14 years 1 months ago
2010
Springer
In a multiple classifier system, dynamic selection (DS) has been used successfully to choose only the best subset of classifiers to recognize the test samples. Dos Santos et al...
MCS
2010
Springer
14 years 1 months ago
2010
Springer
Selecting a set of good and diverse base classifiers is essential for building multiple classifier systems. However, almost all commonly used procedures for selecting such base cla...