Journal ArticleDOI
Text classification based on deep belief network and softmax regression
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TLDR
A novel hybrid text classification model based on deep belief network and softmax regression that can converge at fine-tuning stage and perform significantly better than the classical algorithms, such as SVM and KNN.Abstract:
In this paper, we propose a novel hybrid text classification model based on deep belief network and softmax regression. To solve the sparse high-dimensional matrix computation problem of texts data, a deep belief network is introduced. After the feature extraction with DBN, softmax regression is employed to classify the text in the learned feature space. In pre-training procedures, the deep belief network and softmax regression are first trained, respectively. Then, in the fine-tuning stage, they are transformed into a coherent whole and the system parameters are optimized with Limited-memory Broyden---Fletcher---Goldfarb---Shanno algorithm. The experimental results on Reuters-21,578 and 20-Newsgroup corpus show that the proposed model can converge at fine-tuning stage and perform significantly better than the classical algorithms, such as SVM and KNN.read more
Citations
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A Survey of the Usages of Deep Learning for Natural Language Processing
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Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: A brief overview of text classification algorithms is discussed in this article, where different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods are discussed, and the limitations of each technique and their application in real-world problems are discussed.
Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: An overview of text classification algorithms is discussed, which covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods.
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An Introductory Review of Deep Learning for Prediction Models With Big Data
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Sensor-based and vision-based human activity recognition: A comprehensive survey
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References
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A fast learning algorithm for deep belief nets
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Book ChapterDOI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.