Text classification method and system based on deep learning of hybrid automatic encoder

Abstract

The invention relates to a text classification method and system based on deep learning of a hybrid automatic encoder. The method is a method for combining a sparse restricted Boltzmann machine (SRBM)with a contraction automatic encoder (CAE) to form a hybrid automatic encoder training model, the advantage of feature extraction of the robustness of the contraction automatic encoder (CAE) and theadvantages of feature representation and contrast divergence fast learning of the sparsity of the sparse restricted Boltzmann machine (SRBM) are combined, the learning capability of the hybrid automatic encoder is improved, and the dimension of the feature space is reduced; an unsupervised layer-by-layer greedy learning algorithm is used for training the model, the parameter convergence rate is increased by adding Polyak Averaging when parameters are updated, and fine tuning is conducted on the model by means of a back propagation (BP) algorithm; finally, a support vector machine (SVM) is adopted for classification, the text feature dimension is reduced, and the accuracy of the text classification is improved.

Description

technical field [0001] The invention belongs to the technical field of data classification processing, and in particular relates to a text classification method and system based on hybrid autoencoder deep learning. Background technique [0002] With the rapid development of network technology, massive information resources exist in the form of text. People urgently hope to quickly and effectively find the content they are interested in from the explosive wave of information. As an important research direction of information processing, text classification is a common method to solve text information discovery. However, for massive data, the high dimensionality of features brings many problems to text classification, which cannot meet people's needs for acquiring useful knowledge. [0003] Deep learning is an unsupervised feature learning and feature hierarchy learning method. The unsupervised learning method is generally a feature learning method that realizes feature extr...

AI Technical Summary

Problems solved by technology

[0006] 1. It brings a lot of overhead in training and classification time;

[0007] 2. Too many features often lead to the problem of "dimension disaster" that people often say. High-dimensional problems lead to inaccurate extracted features and reduce the accuracy of classification.

Although the accuracy of text classification is improved, it is still unable to solve the problems of long classification time and low accuracy caused by the high dimensionality and sparseness of massive data features in text classification.

[0009] To sum up, there is still a lack of effective solutions to the problems of long classification time and low accuracy caused by the high dimensionality and sparseness of massive data features in text classification in the prior art.

Images