RNN LSTM-based Deep Hybrid Learning Model for Text Classification using Machine Learning Variant XGBoost

Alagarsamy, Sandhya; James, Visumathi

doi:10.23940/ijpe.22.08.p2.545551

Cited by 3 publications

(1 citation statement)

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“…The second approach is to improve the structure of the model. Such as improving performance through nested models that combine multiple "good enough" models to achieve excellent predictive power, or replacing simple neuron units with complex LSTM neurons, for example, using LSTM models to exploit the advantages of grammar analysis [3][4]. The third is by adjusting the parameters of performance improvement, such as the initialization [5] of an improved model, to ensure that the early gradient has a large number of sparse, or take advantage of the principle of linear algebra [6], to initialize the learning rate, the size of batch size, regularization coefficient, dropout coefficient.…”

Section: Introductionmentioning

confidence: 99%

Optimize the Performance of the Neural Network by using a Mini Dataset Processing Method

Jingliang Chen,

Chenchen Wu

et al. 2023

Journal of Internet Technology

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<p>In the case of traditional methods such as network models and algorithms are highly open source and highly bound to hardware, data processing has become an important method to optimize the performance of neural networks. In this paper, we combine traditional data processing methods and propose a method based on the mini dataset which is strictly randomly divided within the training process; and takes the calculation results of the cross-entropy loss function as the measurement standard, by comparing the mini dataset, screening, and processing to optimize the deep neural network. Using this method, each iteration training can obtain a relatively optimal result, and the optimization effects of each time are integrated to optimize the results of each epoch. Finally, in order to verify the effectiveness and applicability of this data processing method, experiments are carried out on MNIST, HAGRID, and CIFAR-10 datasets to compare the effects of using this method and not using this method under different hyper-parameters, and finally, the effectiveness of this data processing method is verified. Finally, we summarize the advantages and limitations of this method and look forward to the future improvement direction of this method.</p> <p> </p>

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Section: Introductionmentioning

confidence: 99%