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With the development of artificial intelligence algorithm, BP neural network algorithm is widely used in many fields, such as fault diagnosis, intelligent control and dynamic signal processing, because it has many advantages for example self-learning, self-organization and nonlinear mapping. Compared with BP neural network, the hidden Markov model is suitable for dynamic time series modeling and has...
In hyperspectral image classification, labeled samples are usually limited and unlabeled data are available in large quantities. This paper presents a novel semi-supervised feature reduction methods, Semi-supervised Locality Preserving Discriminant Analysis (SLPDA), in order to solve the classification problem of hyperspectral imagery. The distinguished feature of the algorithm is the combination...
Detecting anomaly behavior in large network traffic data has presented a great challenge in designing effective intrusion detection systems. We propose an adaptive model to learn majority patterns under a dynamic changing environment. We first propose unsupervised learning on data abstraction to extract essential features of samples. We then adopt incremental majority learning with iterative evolutions...
So far, the K-means algorithm is the most widely used method for discovering clusters in data, and it has been used extensively in the commercial field, such as customer analysis. However, the efficiency of the algorithm needs to be improved when faced with large amounts of data. The improved algorithm avoids unnecessary calculations by using the triangle inequality. We applies the improved algorithm...
Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data. In this paper, we improved the K-means algorithm. Our improvement is based on the triangle inequality...
Remote sensing technology has become the primary tool for salt marsh vegetation classification at large scales. However, there is still a major problem in differentiating between different spectra for the same vegetation and the same spectrum for different vegetation, when classifying salt marsh vegetation in remotely sensed images. In this paper, two strategies for this problem were proposed. One...
Different doctor may give different diagnosis descriptions on the same result in the medical management system, some even not standardized and lots of the diagnosis are same, doctorpsilas repeated input is inefficiency and can not solve the customerpsilas queuing problem. The traditional K-nearest neighbor algorithm has the drawback of slow recommend while the sample space is large. This paper adopt...
In this paper, an unsupervised fuzzy technique for segmentation of brain magnetic resonance (MR) images is presented, which combines fuzzy clustering algorithm with maximum a posteriori (MAP) criterion. As fuzzy C-means (FCM) tends to balance the number of points in each cluster, cluster centers of smaller clusters are drawn to larger adjacent clusters. In order to overcome this problem occurred in...
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