Text classification is one of the important research issues in the field of text mining, where
the documents are classified with supervised knowledge. In literature we can find many text …

A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …

Automated feature selection is important for text categorization to reduce feature size and to
speed up learning process of classifiers. In this paper, we present a novel and efficient …

This work implements an enhanced hybrid classification method through the utilization of the
naïve Bayes classifier and the Support Vector Machine (SVM). In this project, the Bayes …

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for
text classification. In this paper, we propose a fuzzy similarity-based self-constructing …

Text Categorization is a pattern classification task for text mining and necessary for efficient
management of textual information systems. The documents can be classified by three ways …

Support Vector-based learning methods are an important part of Computational Intelligence
techniques. Recent efforts have been dealing with the problem of learning from very large …

In classical data-driven machine learning methods, massive amounts of labeled data are
required to build a high-performance prediction model. However, the amount of labeled data …

Human occupancy counting is crucial for both space utilisation and building energy
optimisation. In the current article, we present a semi-supervised domain adaptation method …

The difference in data distributions among related, but different domains is a long standing
problem for knowledge adaptation. A new method to transform the source domain …