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In this paper, mixture models are used to classify documents. The basic assumption for the documents in a collection is that each class is composed of a number of mixture components. By identifying the components in the document collection, the classes of documents can thereby be identified from each other. A semi-supervised clustering method is proposed to identify the components (clusters), and...
Internet is becoming a spreading platform for the public opinion. It is important to grasp the Internet public opinion in time and understand the trends of their opinion correctly. Text classification plays a fundamental role in a number of information management and retrieval tasks. But Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information...
With the rapid developing of the network information, it seems to be quite important to provide a more reasonable text classification algorithm for learners. In this paper,we adopt a sensitivity method to modify the characteristic weight in the distance formula and put up with a cutting method of training sample database based on CURE algorithm and Tabu algorithm; then adopt CURE cluster algorithm...
Most conventional incremental learning algorithms perform incremental learning by selecting only one optimized text sample each time, which neither considers the relationship between texts in the unlabeled text set, nor improves incremental learning efficiency. In addition, because of the shortage of the classifierpsilas information storage, the selected optimized text is easily classified incorrectly...
Authorship attribution is the process of determining the writer of a document. In literature, there are lots of classification techniques conducted in this process. In this paper we explore information retrieval methods such as tf-Idf structure with support vector machines, parametric and nonparametric methods with supervised and unsupervised (clustering) classification techniques in authorship attribution...
High-dimensional feature space affects the quality and efficiency of text categorization. This paper investigates an improved genetic algorithm that how to help select relevant features in text classification. We follow the so-called "region growing" method to initialize the population, and uses k-means algorithm to selection operation to control the scope of the search, ensure the validity...
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