Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Network Agents for Learning Semantic Text Classification
Information Retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
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This paper attempts to evaluate machine learning based approaches to text categorization including NTC without decomposing it into binary classification problems, and presents another learning scheme of NTC. In previous research on text categorization, state of the art approaches have been evaluated in text categorization, decomposing it into binary classification problems. With such decomposition, it becomes complicated and expensive to implement text categorization systems, using machine learning algorithms. Another learning scheme of NTC mentioned in this paper is unconditional learning where weights of words stored in its learning layer are updated whenever each training example is presented, while its previous learning scheme is mistake driven learning, where weights of words are updated only when a training example is misclassified. This research will find advantages and disadvantages of both learning schemes by comparing them with each other