MCS
2009
Springer
14 years 6 months ago
2009
Springer
There are a variety of methods for inducing predictive systems from observed data. Many of these methods fall into the field of study of machine learning. Some of the most effec...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
An ensemble is generated by training multiple component learners for a same task and then combining them for predictions. It is known that when lots of trained learners are availab...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
The availability of an ever increasing amount of data sources due to recent advances in high throughput biotechnologies opens unprecedented opportunities for genome-wide gene funct...
MCS
2010
Springer
14 years 6 months ago
2010
Springer
A method for applying weighted decoding to error-correcting output code ensembles of binary classifiers is presented. This method is sensitive to the target class in that a separa...
MCS
2010
Springer
14 years 6 months ago
2010
Springer
Although diversity in classifier ensembles is desirable, its relationship with the ensemble accuracy is not straightforward. Here we derive a decomposition of the majority vote er...
MCS
2010
Springer
14 years 6 months ago
2010
Springer
Abstract. Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from...
MCS
2010
Springer
14 years 6 months ago
2010
Springer
MCS
2010
Springer
14 years 6 months ago
2010
Springer
One of the challenges in unsupervised machine learning is finding the number of clusters in a dataset. Clustering Validity Indices (CVI) are popular tools used to address this pro...
MCS
2010
Springer
14 years 6 months ago
2010
Springer
Abstract. In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional me...
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