This paper investigates an extension of classification trees to deal with uncertain information where uncertainty is encoded in possibility theory framework. Class labels in data s...
Ilyes Jenhani, Nahla Ben Amor, Salem Benferhat, Zi...
Satisfying the basic requirements of accuracy and understandability of a classifier, decision tree classifiers have become very popular. Instead of constructing the decision tree ...
Mihael Ankerst, Christian Elsen, Martin Ester, Han...
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Background: Structure-dependent substitution matrices increase the accuracy of sequence alignments when the 3D structure of one sequence is known, and are successful e.g. in fold ...
The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance between model complexity and available training data. For large vocab...