Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Email overload: exploring personal information management of email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An experimental framework for email categorization and management
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
An intelligent interface for sorting electronic mail
Proceedings of the 7th international conference on Intelligent user interfaces
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Mining e-mail content for author identification forensics
ACM SIGMOD Record
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Combining naive bayes and n-gram language models for text classification
ECIR'03 Proceedings of the 25th European conference on IR research
A comparison of text-categorization methods applied to n-gram frequency statistics
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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This paper reports on experiments in multi-class e-mail categorisation with supervised and unsupervised machine learning techniques. To this end, Support Vector Machines, decision tree learners, instance-based classifiers, Naive Bayes classification approaches and Self-Organising Maps were applied. A word-based and a character n-gram document representation approach were employed in order to assess the categorisation performance of the various learning approaches. The results indicate a substantial increase in classification accuracy when e-mail header information is considered in the document representation. To a much lesser degree, word-based document representations are advantageous over n-gram representations.