Support Vector Machines et Classification d’ Images
نویسنده
چکیده
منابع مشابه
Face Recognition using Eigenfaces , PCA and Supprot Vector Machines
This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...
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Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an effic...
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Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only considers both accuracy and generalization criteria in a single objective fu...
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With the development of digital sensors, an increasing number of high-resolution images are available. Interpretation of these images is not possible manually, which necessitates seeking for practical, fast and automatic solutions to solve the environmental and location-based management problems. The land cover classification using high-resolution imagery is a difficult process because of the c...
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