A quadratic lower bound for Rocchio’s similarity-based relevance feedback algorithm with a fixed query updating factor

Rocchio’s similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In practice, Rocchio’s algorithm often uses a fixed query updating factor. When this is the case, we strengthen the linear Ω(n) lower bound obtained by Chen and Zhu (Inf. Retr. 5:61–86, 2002) and prove that Rocchio’s algorithm makes Ω(k(n−k)) mistakes in searching for a collection of documents represented by a monotone disjunction of k relevant features over the n-dimensional binary vector space {0,1}n, when the inner product similarity measure is used. A quadratic lower bound is obtained when k is linearly proportional to n. We also prove an O(k(n−k)3) upper bound for Rocchio’s algorithm with the inner product similarity measure in searching for such a collection of documents with a constant query updating factor and a zero classification threshold

Fish Classification Algorithm using Single Value Decomposition

Automatic fish classification system plays a very useful role in the process of separating fishes into species for human consumption,ornamentation and other usages. Manual classificationof fishes into different types is difficult and boring. This work proposes a fast and accurate system capable of classifying fish images into distinct classes based on their physical form. The system comprises image-processing, feature extraction and classification method. Fishfeature vector is obtained from Single Value Decomposition (SVD) product extracted from fish block images. Training and testing of the proposed fish classification system are done using Artificial Neural Network (ANN). Experimental test was carried out to determine the species of query fish images. Thirty-six fish images were tested, 94% correct classification result is recorded