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Inspired by recent successful deep learning methods, this paper presents a new approach for polarimetric synthetic aperture radar (PolSAR) image classification. It combines both advantages of pixel-based and object-based methods. An improved simple linear iterative clustering (SLIC) superpixel segmentation algorithm is used to obtain spatial information in the PolSAR image. Then, a Deep Belief Network...
Recently, One-class Support Vector Machine (OC-SVM) has been introduced to detect novel data or outliers. The key problem of training an OC-SVM is how to solve the constrained quadratic programming problem. The optimization process suffers from the problem of memory and time consuming. We present a new method to efficiently train the OC-SVM. Based on the random sampling lemma, the training dataset...
Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we...
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