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Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain–computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount...
In modern networks, there exist different applications which generate various different types of network traffic. In order to improve the performance of network management, it is important to identify and classify the internet traffic. The machine learning (ML) technique based on per-flow statistics has been widely used in traffic classification. Different from traditional classification methods,...
Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by...
Information Gain algorithm for text feature selection usually leads to some features which are low-frequency in the designated category but high-frequency in other categories to be selected , this is clearly not the desired results for feature selection. To overcome the shortage, this paper proposes an improved IG approach based on Compensation Factor and Penalty Factor for feature distribution. An...
In this paper, we propose a novel approach to identifying user intents of search engine queries. Specifically, we recast it as a classification problem, in which four types of features are adopted. The classification features are based on deep linguistic analysis of queries as well as search engine feedbacks. We evaluate the method with the real web query data. The results show that about 88% of the...
When we apply SVM (support vector machine) method to filter spam, there will be a problem that data sets are too large to be solved in the algorithm. So we present a novel approach which is a method of combination of LSA (latent semantic analysis) and FSVM (fuzzy support vector machine). In the process of data set building, we adopt LSA method to handle the problem which data sets lies in implicit...
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