… support vector machines are generalized to decision trees. A support vector machine is
usedforeach decision in the tree… The “optimal ’’ decision tree is characterized, and both a primal …

… In this paper we investigate and evaluate the decision tree data mining techniques as an …
it with Support Vector Machines (SVM). Intrusion detection with Decision trees and SVM were …

… Namely, the division of the feature space depends on the structure of a decision tree. To …
nodes of a decision tree. For this, we propose four types of decision trees based on separability …

… classifier (MLC), support vector machines (SVMs) and the decision trees (DTs). The DTs in
… of the “best” tree structure and the decision boundaries. Recent developments in the field of …

… the accuracy in classification is not achieved, hence we are proceeding with the supervised
learning techniques like support vector machine, decision tree algorithm and clustering …

… In this study, classification models based on decision trees and support vector machines (…
This study is one of the firsts to compare the performance of SVM and decision tree methods …

… (in particular, support vector machines), decision trees, and meta-… the classification problem
[10]. Although these techniques are often mathematically equivalent (eg, both decision trees …

… , hypertext, and text segregation and classification problems. These algorithms are quite …
the Support Vector Machine algorithm is intended for regression and classification problems…

… decision trees. We also substantially improve upon previously proposed protocols for
hyperplane based classifiers—we include support vector machines and logistic regression …

… Later we present the theoretical framework of support vector machines that, although strictly
… we retain the classification decision tree, our proposal uses support vector machines for …