incremental query evaluation for support vector machines
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the system first processes k sampling queries and top- k uncertain queries to select the candidate data items for training. The user's top- k relevant queries are then evaluated to compute the answer. This approach has shown to be effective. However, it suffers from the scalability problem associated with larger database sizes. To address this limitation, we propose an incremental query evaluation technique for these three types of queries. Based on the observation that most queries are not revised dramatically during the iterative evaluation, the proposed technique reuses the results of previous queries to reduce the computation cost. Furthermore, this technique takes advantage of a tuned index structure to efficiently prune irrelevant data. As a result, only a small portion of the data set needs to be accessed for query processing. This index structure also provides an inexpensive means to process the set of candidates to evaluate the final query result. This technique can work with different kernel functions and kernel parameters. Our experimental results indicate that the proposed technique significantly reduces the overall computation cost, and offers a promising solution to the scalability issue.
support vector machines multimedia retrieval active learning relevance feedback