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The main objective of the spatial image classification is to extract information classes from a multiband raster spatial image. The network structure and number of inputs are the key factors in deciding the performance and accuracy of the traditional pixel based image classification techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), Fuzzy logic, Decision Trees (DT) and...
The increasing number of training algorithms along with their convincing results will make this question that which algorithm will be more efficient. This study aims to perform some widespread tests on some well-known training algorithms (Levenberg - Marquardt, Resilient back propagation and Scaled conjugate gradient) to evaluate their performance for scene illumination classification. The results...
In this paper, we proposed a method which incorporated multi-scale analysis into neural nets to solve the problem that fractal coding allows fast decoding but suffers from long encoding times. This method can reduce the computational load of fractal image coding significantly though efficient classification of image improve speed of image scan. Furthermore this paper also incorporates gray relational...
In this paper, the classification results obtained from several kinds of support vector machines (SVM) and neural networks (NN) are compared with our proposed classifier. Our approach is based on neural networks and interval neutrosophic sets which are used to classify the input patterns into one of the two binary class outputs. The comparison is based on several classical benchmark problems from...
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