The nature of statistical learning theory
The nature of statistical learning theory
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Adaptive anti-spam filtering for agglutinative languages: a special case for Turkish
Pattern Recognition Letters
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
The evidence framework applied to classification networks
Neural Computation
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
Study on Ensemble Classification Methods towards Spam Filtering
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Automatic checking of alternative texts on web pages
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs: Part I
Using biased discriminant analysis for email filtering
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
PCA document reconstruction for email classification
Computational Statistics & Data Analysis
An effective spam filter based on a combined support vector machine approach
International Journal of Internet Technology and Secured Transactions
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
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The growth of email users has resulted in the dramatic increasing of the spam emails during the past few years. In this paper, four machine learning algorithms, which are Naive Bayesian (NB), neural network (NN), support vector machine (SVM) and relevance vector machine (RVM), are proposed for spam classification. An empirical evaluation for them on the benchmark spam filtering corpora is presented. The experiments are performed based on different training set size and extracted feature size. Experimental results show that NN classifier is unsuitable for using alone as a spam rejection tool. Generally, the performances of SVM and RVM classifiers are obviously superior to NB classifier. Compared with SVM, RVM is shown to provide the similar classification result with less relevance vectors and much faster testing time. Despite the slower learning procedure, RVM is more suitable than SVM for spam classification in terms of the applications that require low complexity.