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Transmission line galloping often causes structural and electrical failures, which is a serious threat to the security of transmission systems. Through analysing the influence factors of galloping, it reveals that weather conditions are the most significant excitation factors and conductors of any voltage level and region may gallop when the apt-galloping weather conditions are satisfied. This study...
We present an intensity neighborhood-based system for segmenting arbitrary biomedical image datasets using supervised learning. Because neighborhood methods are often associated with high-dimensional feature vectors, we explore a Principal Component Analysis (PCA) based method to reduce the dimensionality (and provide computational savings) of each neighborhood. Our results show that the system can...
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data...
The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural...
Active learning is a promising tool to improve the performance of content-based image retrieval (CBIR). As a commonly used active learning approach, angle-diversity provides the most informative images to user for feedback. However, it suffers from the problem that the query concept is diverse and the numbers of the positive and the negative images are imbalanced. As a consequence, the positive samples...
Support vector machine (SVM) provides accurate classification but suffers from a large amount of computation. In this paper we propose here an incremental procedure for growing support vector classifiers, which serves to avoid a priori architecture estimation or the application of a pruning mechanism after SVM training. The proposed growing approach also opens up new possibilities for dealing with...
Based on analyzing the relationship between the Karush-Kuhn-Tucker (KKT) conditions of support vector machine and the distribution of the training samples, the possible changes of support vector set after new samples are added to training set was analyzed, and the generalized Karush-Kuhn-Tucker conditions was defined. Based on the classification equivalence between the previous training set and the...
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