A Dropout Optimization Algorithm to Prevent Overfitting in Machine Learning

Abstract

In the computer field, data encryption has always been a hot research topic, and TreScriptic algorithm has become the object of attention and research because of its simple parameters, clear classification and other advantages. This paper mainly introduces the web server which is built based on different models and has good performance, does not affect the system performance, has strong portability, and can be widely used in most occasions. This paper proposes a genetic NSBP neural network based on evolutionary algorithm, which is over fitting multi-dimensional and has global optimization characteristics. In this algorithm, parameters are regarded as input variables rather than directly interacting with output samples. Then this paper designs the Dropout optimization algorithm process and tests the performance. The test results show that the performance of the Dropout optimization algorithm based on machine learning to prevent over fitting takes about 5 seconds to process data, and the time to prevent over fitting is at least 3 seconds. This method can improve the biological signal processing effect of the over fitting.

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

“…This study adopted a similar CNN architecture with the distinction that character embeddings were used solely as inputs to the CNN, omitting character-type features. Additionally, a dropout layer was applied before feeding the character embeddings into the CNN [26]. Finally, the neural network model was constructed by feeding the BLSTM output vectors into a CRF layer.…”