With the increasing availability of electronic documents and the rapid growth of the World
Wide Web, the task of automatic categorization of documents became the key method for …

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and
then searching for linear relations among the embedded data points. The embedding is …

Abstract Machine learning approaches to multi-label document classification have to date
largely relied on discriminative modeling techniques such as support vector machines. A …

This paper presents the implementation of a new text document classification framework that
uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean …

Logistic Regression is a well-known classification method that has been used widely in
many applications of data mining, machine learning, computer vision, and bioinformatics …

This work implements a new text document classifier by integrating the K-nearest neighbor
(KNN) classification approach with the support vector machine (SVM) training algorithm. The …

Abstract ℓ1-regularized logistic regression, also known as sparse logistic regression, is
widely used in machine learning, computer vision, data mining, bioinformatics and neural …

The standard maximum margin approach for structured prediction lacks a straightforward
probabilistic interpretation of the learning scheme and the prediction rule. Therefore its …

High Relevance Keyword Extraction (HRKE) facility is introduced to Bayesian text
classification to perform feature/keyword extraction during the classifying stage, without …

Constructing a single text classifier that excels in any given application is a rather inviable
goal. As a result, ensemble systems are becoming an important resource, since they permit …