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Locally linear embedding is often invalid for sparse data sets because locally linear embedding simply takes the reconstruction weights obtained from the data space as the weights of the embedding space. This paper proposes an improved method for sparse data sets, a united locally linear embedding, to make the reconstruction more robust to sparse data sets. In the proposed method, the neighborhood correlation matrix presenting the position information of the points constructed from the embedding space is added to the correlation matrix in the original space, thus the reconstruction weights can be adjusted. As the reconstruction weights adjusted gradually, the position information of sparse points can also be changed continually and the local geometry of the data manifolds in the embedding space can be well preserved. Experimental results on both synthetic and real-world data show that the proposed approach is very robust against sparse data sets. [ABSTRACT FROM AUTHOR]

This paper presents a post-processing system for improving the recognition rate of a Handwritten Chinese Character Recognition (HCCR) device. This three-stage hybrid post-processing system reduces the misclassification and rejection rates common in the single character recognition phase. The proposed system is novel in two respects: first, it reduces the misclassification rate by applying a dictionary-look-up strategy that bind the candidate characters into a word-lattice and appends the linguistic-prone characters into the candidate set; second, it identifies promising sentences by employing a distant Chinese word BI-Gram model with a maximum distance of three to select plausible words from the word-lattice. These sentences are then output as the upgraded result. Compared with one of our previous works in single Chinese character recognition, the proposed system improves absolute recognition rates by 12%. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to image texture classification, which involves a model of artificial organisms i.e. Artificial Crawlers (ACrawlers) and a series of evolution curves representing the features of the texture. The distributed ACrawlers locally interact with their living environment, i.e. textured regions, and each ACrawler acts according to a set of homogenous rules for isotropic motion, energy absorption and colony formation, etc. The ACrawlers evolve through natural selection, which produces the specific curves of agent evolution, habitant settlement and colony formation as well as the scale distribution of all colonies. The feasibility and effectiveness of the proposed method have been demonstrated by experiments. [ABSTRACT FROM AUTHOR]

An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated. [ABSTRACT FROM AUTHOR]

This paper reports on an experimental approach to find a modularized artificial neural network solution for the UCI letters recognition problem. Our experiments have been carried out in two parts. We investigate directed task decomposition using expert knowledge and clustering approaches to find the subtasks for the modules of the network. We next investigate processes to combine the modules effectively in a single decision process. After having found suitable modules through task decomposition we have found through further experimentation that when the modules are combined with decision tree supervision, their functional error is reduced significantly to improve their combination through the decision process that has been implemented as a small multilayered perceptron. The experiments conclude with a modularized neural network design for this classification problem that has increased learning and generalization characteristics. The test results for this network are markedly better than a single or stand alone network that has a fully connected topology. [ABSTRACT FROM AUTHOR]

Focuses on developments regarding the "IEEE Transactions on Pattern Analysis and Machine Intelligence" journal as of March 2004. Finding that the journal is the highest ranked IEEE transactions among 338 computer science titles, according to the 2002 Journal Citation Report; Retirements and additions to associate editors; Decision by the publications board to charge a mandatory fee of $200 per page for papers over 12 pages.