基于TDMA數(shù)據(jù)鏈的文本分類系統(tǒng)研究與應(yīng)用
[Abstract]:Wireless equipment is widely used in various fields because of its simple operation, easy to carry, convenient exchange of information and so on. It is an indispensable means of communication for the military. Therefore, wireless equipment is a common command and liaison tool in both military war and disaster relief. In order to transmit data between wireless devices more quickly and reliably, we construct a wireless network system based on TDMA (Time Division Multiple Access, (time Division multiple access) data link. The research content of this paper mainly includes the protocol design and implementation between the service platform and wireless platform, and the research and implementation of the Chinese text classification system on the service platform. The details are as follows: 1) in the wireless network system based on TDMA data link, each type of terminal includes wireless platform and service platform. The wireless platform completes the transmission of terminal information, and the business platform is responsible for processing files and locating information. The management and transmission of audio and video services, in order to ensure the reliable transmission of each service, we designed SWIP (Service Wireless Interface Protocol) protocol. 2) in order to filter and classify the transmission content, The Chinese text classification system needs to be installed on the business platform. Firstly, the Chinese text classification system is introduced, and various modules in the system, such as text preprocessing, feature dimensionality reduction, text representation, classification algorithm and so on, are described in detail. A new Chinese text classification system is proposed, in which the LDA (Latent Dirichlet Allocation) topic model is used to represent the text and the support vector machine (Support Vector Machines, is used to represent the text. SVM) and K-nearest neighbor algorithm (K-nearest neighbor classification) combined with the algorithm KSVM to classify. In order to analyze the performance of the proposed Chinese text classification system, the evaluation criteria such as accuracy, recall rate, F1-measure and so on need to be calculated experimentally. This paper presents two comparative experiments: the analysis of classification effect based on different classification algorithms based on LDA subject model and the analysis of KSVM classification effect based on different text models. By analyzing the experimental data, we can see that the Chinese text classification system based on LDA topic model and KSVM classification algorithm can achieve better classification effect.
【學(xué)位授予單位】:北京郵電大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2016
【分類號(hào)】:TN919.2;TP391.1
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