The nature of statistical learning theory
The nature of statistical learning theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
| Hi-index | 0.00 |
Three methods are investigated and presented for automated learning of Relevance Vector Machines (RVM) in large scale text sets. RVM probabilistic Bayesian nature allows both predictive distributions on test instances and model-based selection yielding a parsimonious solution. However, scaling up the algorithm is not workable in most digital information processing applications. We look at the properties of the baseline RVM algorithm and propose new scaling approaches based on choosing appropriate working sets which retain the most informative data. Incremental, ensemble and boosting algorithms are deployed to improve classification performance by taking advantage of the large training set available. Results on Reuters-21578 are presented, showing performance gains and maintaining sparse solutions that can be deployed in distributed environments.