@article{Silva2007,
	title = {Rvm ensemble for text classification},
	author = {Catarina Silva and Bernadete Ribeiro},
	journal = {International Journal of Computational Intelligence Research},
	number = {1},
	pages = {31--35},
	url = {http://www.ripublication.com/ijcirv3/ijcirv3n1_7.pdf},
	volume = {3},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/26333bc0594ed5ca0d6026f380cb9ecec/marcoalvarez},
	abstract = {Automated classification of texts by their likeness or affinity has
	greatly eased the management and processing of the massive volumes
	of information we face everyday. Although Support Vector Machines
	(SVM) provide a state-of-the-art technique to tackle this problem,
	Relevance Vector Machines (RVM), which rely on Bayesian inference
	learning, offer advantages such as their capacity to find sparser
	and probabilistic solutions. A known problem with the Bayesian approaches,
	however, is their relative inability to scale to larger problems
	where millions of documents are involved as well as real-time user's
	requests. We propose an ensemble strategy to circumvent RVMs scalability
	problem by applying a divide-and-conquer technique to handle the
	overload of available data, where the training documents are divided
	amongst small RVM classifiers, then the ensemble combines their individual
	contributions. The solution achieved keeps a sparse decision function
	and is computationally efficient. Results with respect to Reuters-21578
	clearly demonstrate the proposed strategy can surpass other techniques,
	in both in terms classification performance and response time.},
	keywords = {Classification RVM }
}