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Classification of motor imagery (MI)-based electroencephalogram (EEG) signals is a key issue for the development of brain-computer interface (BCI) systems. The objective of this study is to develop an algorithm that can distinguish two categories of MI EEG signals. In this paper, we propose a new classification algorithm for two-class MI signals recognition in BCIs. The proposed scheme develops a...
this paper presents a classification based on support vector machine (SVM) to carry out comprehensive analysis of the ability of enterprises paying debt,reduce the risk of bank to provide a loan. First this paper introduces the main principle of support vector machines to establish data classification model, using historical data for classification. Then collect the financial indices of 80 enterprises...
Previous work has shown that application of graph mining techniques to system dependence graphs improves the precision of automatic defect discovery by revealing subgraphs corresponding to implicit programming rules and to rule violations. However, developers must still confirm, edit, or discard reported rules and violations, which is both costly and error-prone. In order to reduce developer effort...
In this paper, we propose a feature generation and classification approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. The GA is utilized to select a subset of candidate features, a subset of candidate transformations to generate new features. The logistic regression model and Bayesian network model are then used as the classifier. Experimental results show...
Background: Outcome prediction for subarachnoid hemorrhage (SAH) helps guide care and compare global management strategies. Logistic regression models for outcome prediction may be cumbersome to apply in clinical practice. Objective: To use machine learning techniques to build a model of outcome prediction that makes the knowledge discovered from the data explicit and communicable to domain experts...
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