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With the development of next-generation sequencing technologies, large number of transcripts has been accumulated in public databases. Long non-coding RNAs (lncRNAs), typically above 200 nucleotides in sequence length, have recently attracted increasing interests because of their important roles in various cellular processes. While it is straightforward to distinguishing lncRNAs from most small non-coding...
This paper develops a method to learn very few discriminative part detectors from training videos directly, for action recognition. We hold the opinion that being discriminative to action classification is of primary importance in selecting part detectors, not just intuitive. For this purpose, part selection based on feature selection is proposed, employing SVM method. Firstly, large number of candidate...
Network traffic classification plays an extremely important role in network management and service. Support vector machine (SVM) is widely adopted to classify traffic flows for its high accuracy. All features selected are treated equally in traditional SVM network traffic classification, which take little consideration of that each feature exerts a different influence on classification. Therefore,...
In the wireless sensor network (WSN), the operation reliability is usually evaluated by processing measured datas at network nodes. As the traditional algorithms exist the problems of the complex calculation and large energy consumption, a method for fault diagnosis of nodes in WSN based on rough set theory (RS) and support vector machine (SVM) is proposed in this paper. In this paper, we collect...
While BMI systems abound, little care has been exercised over practical considerations in the day to day use of such systems. This paper proposes to learn a Start-Stop switch to augment a directional BMI. Taken together, the hope is that a BMI could be constructed that would be able to signal the appropriate directional intent when called upon and be virtually silent when not needed. Using data from...
In our previous work, a non-stereotypical brain machine interface system was implemented with freely-moving rats, and a nonlinear support vector machine (SVM) classifier was used to map neural signals in the rats' motor cortices onto a set of discrete classes of directions (left and right). In this paper, we provide a comprehensive analysis about the selection of neurons and temporal parameters, which...
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