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Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
We investigate the utility of a novel form of prior, namely the accuracies with which humans categorize briefly displayed images. Such information reflects the complexity of an image for the visual system and carries information about the features important for categorization. We incorporate the prior in an SVM framework, by biasing the decision boundary towards examples difficult for humans, and...
Classifying the heterogeneous classes present in the hyper spectral image is one of the recent research issues in the field of remote sensing. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes present in the hyper spectral image are having different textures, textural classification is entertained. Wavelet based...
Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number...
This paper presents a new method for hyperspectral image classification. It combines support vector machine (SVM), particle swarm optimization (PSO), and genetic algorithm (GA) together. Its aim is to improve the classification accuracy and reduce the computation consumption based on heuristic algorithms. Because the classification accuracy is impacted by the parameters of the SVM model and feature...
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