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Tor is an anonymous communication system that can protect our privacy, but it also provides a haven for criminals to avoid network tracing. Therefore, anonymous traffic analysis and classification is an important part of maintaining network security. Existing Tor traffic classification methods require a large number of labeled data, and the classification accuracy rate is not satisfied for practical...
How to embed the new observations (or samples) into the low-dimensional space is a crucial problem in non-linear manifold learning techniques. This issue can be converted into the problem of finding an accurate mapping that transfers the unseen data samples into an existing manifold. In this paper, a locality-constrained sparse representation algorithm is proposed to deal with the out-of-sample embedding...
Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture...
In the Brain-computer interface, classification and recognition technology plays an important role, especially the EEG classification and recognition for the movement imagery. In this paper, we use a new type of sensors to collect EEG signals. According to imagine the movement of left or right hand to identify two types of thinking, we proposed a new recognition method based on AR(auto-regressive)...
Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fisher's linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed...
In the process of iris classification, a new classification distance with adjustable weight which takes advantage of whole phase information to encode is proposed. The method is to use feature extraction function to do the extraction toward all iris image, which could obtain real and imaginary part iris information. Then, tangent function is used to transform extracted real and imaginary part characteristics...
A pulmonary nodule is relatively round lesion, or area of abnormal tissue located within the lung that can be seen in thoracic CT scans. Because noise and same like disturbance of blood vessels and tracheas, detection of the lung nodule is difficult. A three-dimensional pulmonary nodule detection method for thoracic CT scans is proposed in this paper. First, bounding box method and three-dimensional...
This paper expounds the principle of BP neural network with applications to image compression and the neural network models. Then an image compressing algorithm based on improved BP network is developed. The blocks of original image are classified into three classes: background blocks, object blocks and edge blocks, considering the features of intensity change and visual discrimination. The BP algorithm...
The idea of representing images using a bag of visual words is currently popular in object category recognition. Since this representation is typically constructed using unsupervised clustering, the resulting visual words may not capture the desired information. Recent work has explored the construction of discriminative visual codebooks that explicitly consider object category information. However,...
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