Year: 2018
Type: article
Abstract: This paper compares traditional machine learning models, i.e. Support Vector Machine, k-Nearest Neighbors, Decision Tree and Random Forest, with Feedforward Neural Network and Long Short-Term Memory. We observe that the two neural networks achieve higher accuracies than traditional models. This paper also tries to figure out whether dropout can improve accuracy of neural networks. We observe that for Feedforward Neural Network, applying dropout can lead to better performances in certain cases but worse performances in others. The influence of dropout on LSTM models is small. Therefore, using dropout does not guarantee higher accuracy.
Source: Modern Economy
Author: Ruilin Liu
Institution: University of Southern California
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W2900843648