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Recent developments in Graphics Processing Units (GPUs) have enabled inexpensive high performance computing for general-purpose applications. Due to GPU's tremendous computing capability, it has emerged as the co-processor of CPU to achieve a high overall throughput. CUDA programming model provides the programmers adequate C language like APIs to better exploit the parallel power of the GPU. K-nearest...
A new text classification model based on training samples selection and feature weight adjustment is presented. First it computes representativeness score of samples so as to distinguish noise samples from original training samples. Then a feature weight adjustment taking inter-class distribution and intra-class distribution into consideration is used to further improve the performance of text classification...
Feature selection is of paramount concern in document classification process which improves the efficiency and accuracy of text classifier. Vector Space Model is used to represent the ??Bag of Word?? BOW of the documents with term weighting phenomena. Documents representing through this model has some limitations that is, ignoring term dependencies, structure and ordering of the terms in documents...
This article introduced the text excavation's research condition, has analyzed the text excavation basic concept and the technology, summarized the text excavation process, the commonly used algorithm, the text classification, the text cluster, the connection analysis, the tendency forecast and so on, pointed out that the algorithm the insufficiency, has forecast the text excavation futurology question...
This work proposes an approach to address the problem of inductive bias or model misfit incurred by the centroid classifier assumption to enhance the automatic text classification task. This approach is a trainable classifier, which takes into account tfidf as a text feature. The main idea of the proposed approach is to take advantage of the most similar training errors to the classification model...
Latent Dirichlet Allocation (LDA) is a generative model employing the symmetry Dirichlet distribution as prior of the topic-words' distributions to implement model smoothing. When LDA is applied to text classification, smoothing is essential to classification performance. In this paper, we propose a feature-enhanced smoothing method in the idea that words not appeared in the training corpus can help...
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are input into decision level, the last classification result is output.This model is applied into improving...
Traditional discriminative classification method makes little attempt to reveal the probabilistic structure and the correlation within both input and output spaces. In the scenario of multi-label classification, most of the classifiers simply assume the predefined classes are independently distributed, which would definitely hinder the classification performance when there are intrinsic correlations...
In the text literature, many Bayesian generative models were proposed to represent documents and words in order to process text effectively and accurately. As the most popular one of these models, Latent Dirichlet Allocation Model(LDA) did great job in dimensionality reduction for document classification. In this paper, inspiring by latent Dirichlet allocation model, we propose LDCM or latent Dirichlet...
Text representation is the basis of text processing. Most current text representation models ignore the words' inter-relations, which result in the loss of textpsilas structure information. This paper proposed a novel text representation model, which uses lexical network to represent the text and retains the text's structure. According to the different levels of words' inter-relations, co-occurrence...
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