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76.833  Articles
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A 13-itemed scenario framework for classifier single training parameter optimization is developed. Formally, the problem is to find global extremum (mostly, minimum) of function as a classifier output parameter against its single training parame... see more

In recent years, support vector machine (SVM) based on empirical risk minimization is supervised learning model which has been successfully used in the classification and regression. The standard soft-margin SVM trains a classifier by solving an optimizat... see more

Context of this article topics is that the issue of the choice of parameters of the extractor features and classification analysis algorithm in conditions of a priori uncertainty, resource constraints, and information is not enough studied and in full has... see more

Context. The questions of adapting the convolution neural network classifier use in automatic speaker recognition system of critical use(ASRSCU) are considered. The research object is the individual features of the human speech process.Objective. Developm... see more

Purpose of the article: The ability of the company to predict customer churn and retain customers is considered to be worthy competitive advantage since it improves cost allocation in customer retention programs, retaining future revenue and profits. In a... see more

Feature extraction plays an important role in the sentiment analysis process, especially of text data. The Naive Bayes Classifier performs well on low feature dimensions. However, the accuracy provided is not optimal. To acquire  optimal machine lear... see more

This study aims to determine the effect of covid 19 on the movement of the JCI Stock Price by testing various combinations of the input variables of closed price stock data on the JCI. The analysis is carried out to find the best RMSE value from the combi... see more

This paper focuses on the process of adjusting weights and biases of feed-forward ANN during their training process. A new algorithm for tuning artificial neural networks parameters has been proposed to overcome some limitations of existing optimization a... see more

Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS) such as low detection capability... see more

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