Improving perceptron convergence algorithm for retrieval systems
Based on the premises of relevance feedback, we investigate a well-known learning procedure called the Perceptron Convergence Algorithm (PCA) (Wong et al., 1988) for information retrieval (TR) systems. Although PCA is one of the simplest methods with the least overhead, it has its disadvantages. One of the main disadvantages is that the rate of convergence of the algorithm can be slow. In this paper, we propose and evaluate an algorithm that can be used in IR systems, called the Fast Perceptron, which improves the speed of convergence of the PCA. By considering a prototype image retrieval system called Web-IDBS (Image Database System) (Guivada et al., 1994) that supports content based adaptive retrieval using semantic attributes (Guivada and Raghavan, 1995), we compared the performance of the fast perception algorithm to that of the PCA. The objective of this experiment was to compare the impact of user relevance feedback on improving retrieval effectiveness with these query reformulating algorithms. The metrics used for comparison were the number of iterations required in each algorithm to get the optimal query, the final ranking of the target image, and the number of images for which the user provides feedback. The results demonstrated that significant improvement is achieved in the metrics considered. (C) 1999 Elsevier Science B.V. All rights reserved.
perceptron convergence relevance feedback fast perceptron information retrieval system