Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Automatic indexing based on Bayesian inference networks
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
WordNet: a lexical database for English
Communications of the ACM
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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This work presents an application example of text document filtering. We compare the DIMLP neural hybrid model to several machine learning algorithms. The clear advantage of this neural hybrid system is its transparency. In fact, the classification strategy of DIMLPs is almost completely encoded into the extracted rules. During cross-validation trials and in the majority of the situations, DIMLPs demonstrated to be at least as accurate as support vector machines, which is one of the most accurate classifiers of the text categorization domain. In the future, in order to further increase DIMLP accuracy, we believe that common sense knowledge could be easily inserted and refined with the use of symbolic rules.