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Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields. In some cases they allow constructing more precise forecasting models, leveraging the predictive potential of many variables. Unfortunately, in practice we do not know which observed predictors have a direct impact on the target variable. Moreover, adding unrelated variables may diminish the quality...
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
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