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The Support Vector Machines (SVM) become popular E-Business data mining tools recently, and the datasets of E-Business are usually large-scale. If Support Vector Machines are trained on large-scale datasets, the training time will be very long and the classifier's accuracy will become lower too. As training a large-scale SVM is equated to solve a large-scale quadratic programming (QP) problem, so...
Pattern recognition applications such as natural phenomena detection and structural health monitoring have been widely applied using wireless sensor networks. These applications involve large amount of data to be analysed, and thus incur high computational time and complexity. In this paper, we present a parallel associative memory-based pattern recognition algorithm known as distributed hierarchical...
This paper studies the bitonic merge sort algorithm which is difficult to get the prospective sorted result for some odd sequence. We develop a new parallel sorting algorithm, increasing CCI (compare and conditionally interchange) operations, derived from the odd merge sort algorithm. The proposed algorithm is based on a divide-and-conquer strategy. First of all, the sequence to be sorted is decomposed...
In the field of digital speech recognition powerful ASR (automatic speech recognizer) systems have been developed which employ highly intricate algorithms like the HMM, DTW and neural network based algorithms capable of recognizing up to 1000 different words. Their high complexity and computation requirements prove to be superfluous for less demanding tasks. In this paper is proposed a simple, less...
All-to-all personalized communication, also known as complete exchange, is one of the most dense communication patterns in parallel computing. In this paper, we propose new indirect algorithms for complete exchange on all-port ring and tori. The new algorithms fully utilize all communication links and transmit messages along shortest paths to completely achieve the theoretical lower bounds on message...
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