Now showing items 1-5 of 5
Regularizing soft decision trees
Recently, we have proposed a new decision tree family called soft decision trees where a node chooses both its left and right children with different probabilities as given by a gating function, different from a hard ...
Design and analysis of classifier learning experiments in bioinformatics: survey and case studies
(IEEE COMPUTER SOC, 2012-12)
In many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design ...
Eigenclassifiers for combining correlated classifiers
(ELSEVIER SCIENCE INC, 2012-03-15)
In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ulas, M. Semerci, O.T. Yildiz, E. Alpaydin, Incremental construction of ...
Unsupervised morphological analysis using tries
(Springer London, 2011-09-26)
This article presents an unsupervised morphological analysis algorithm to segment words into roots and affixes. The algorithm relies on word occurrences in a given dataset. Target languages are English, Finnish, and Turkish, ...
Statistical tests using hinge/ε-sensitive loss
Statistical tests used in the literature to compare algorithms use the misclassification error which is based on the 0/1 loss and square loss for regression. Kernel-based, support vector machine classifiers (regressors) ...