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In this paper, we propose a new learning algorithm for the Subspace Pattern Recognition Method (SPRM) called the Hebbian Learning Subspace Method (HLSM). It uses the notion of a weighted squared orthogonal projection distance which gives different weightages to different basis vectors in the computation of the orthogonal projection distance. The principle applied during learning is the same as that...
In this paper, we describe the applicability of the K-means clustering algorithm for locating thresholds in a given histogram. In order to find optimal thresholds a probabilistic method called Multi-state Stochastic Connectionist Approach (MSCA) is employed. Mean Field Annealing (MFA), a deterministic counterpart of MSCA, is also studied in this context. A parallel model to parallelize the above...
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