Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models
Classifying news stories using memory based reasoning
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
IEEE Intelligent Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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This research proposes the application of NTC (Neural Text Categorizer) for categorizing news articles. Even if the research on text categorization has been progressed very much, documents should be still encoded into numerical vectors. Encoding so causes the two main problems: huge dimensionality and sparse distribution. The idea of this research as the solution to the problems is to encode documents into string vectors and apply the NTC as a string vector based approach to text categorization. The idea will be described in detail and validated.