A modified and fast Perceptron learning rule and its use for Tag Recommendations in Social Bookmarking Systems

A modified and fast to converge Perceptron learning rule algorithm is proposed as a general classification algorithm for linearly separable data. The strategy of the algorithm takes advantage of training errors to successively refine an initial Perceptron Classifier. Original Perceptron learning rule uses training errors along with a parameter α (learning rate parameter that has to be determined) to define a better classifier. The proposed modification does not need such a parameter (in fact it is automatically determined during execution of the algorithm). Experimental evaluation of the proposed algorithm on standard text classification collections, show that results compared favorably to those from state of the art algorithms such as SVMs. Experiments also show a significant improvement of the convergence rate of the proposed Perceptron algorithm compared to the original one. Seeing the problem of this year’s Discovery Challenge (Tag Recommendation), as an automatic text classification problem, where tags play the role of categories and posts play the role of text documents, we applied the proposed algorithm on the datasets for Task 2. In this paper we briefly present the proposed algorithm and its experimental results when applied on the Challenge’s data.