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Based on the real situation of radio telegraphic training and using the net classroom, this paper proposes a design of radio telegraphic training and an assessment system which adopts C/S mode. It conducts careful analysis on various key technologies, such as manual key keying signal processing, MORSE code store-and-forward and intersystem data interaction, and put forwards corresponding solutions...
Hidden Markov models (HMM) have been widely used in natural language processing (NLP), especially in syntactic level applications, which appears naturally as short-range-dependent sequence recognition problems. But the structure of HMM limits the usage of global knowledge including the sentiment analysis of the text, which has become an increasingly popular research topic in NLP now. In this paper,...
We propose a new approach to re-optimize Hidden Markov Models (HMMs) using Evolutionary Computation methods. The hybrid algorithm iterates in the neighborhood of original HMMS parameters with a fitness function that evaluates the solution of sequence recognition by knowledge as well as by likelihood. Experiments on POS tagging show that the parameters weighted system outperforms the baseline of the...
This paper proposes a rough set reduction scheme for Support Vector Machine (SVM). In the proposed scheme, SVM is used for the classification task based on the significance of each feature vector, while rough set is applied to improve feature selection and data reduction. Particle Swarm Optimization (PSO) is used to optimize the rough set feature reduction. The proposed approach is used to classify...
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