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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
A New Fuzzy Membership Assignment And Model Selection Approach Based On Dynamic Class Centers For Fuzzy Svm Family Using The Firefly Algorithm, Omid Naghash Almasi, Modjtaba Rouhani
A New Fuzzy Membership Assignment And Model Selection Approach Based On Dynamic Class Centers For Fuzzy Svm Family Using The Firefly Algorithm, Omid Naghash Almasi, Modjtaba Rouhani
Turkish Journal of Electrical Engineering and Computer Sciences
The support vector machine (SVM) is a powerful tool for classification problems. Unfortunately, the training phase of the SVM is highly sensitive to noises in the training set. Noises are inevitable in real-world applications. To overcome this problem, the SVM was extended to a fuzzy SVM by assigning an appropriate fuzzy membership to each data point. However, suitable choice of fuzzy memberships and an accurate model selection raise fundamental issues. In this paper, we propose a new method based on optimization methods to simultaneously generate appropriate fuzzy membership and solve the model selection problem for the SVM family in linear/nonlinear …
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
Turkish Journal of Electrical Engineering and Computer Sciences
Face recognition is an essential issue in modern-day applications since it can be used in many areas for several purposes. Many methods have been proposed for face recognition. It is a difficult task since variations in lighting, instantaneous mimic varieties, posing angles, and scaling differences can drastically change the appearance of the face. To suppress these complications, effective feature extraction and proper alignment of face images gain as much importance as the recognition method choice. In this paper, we provide an extensive comparison of the state-of-the-art face recognition methods with the most well-known techniques used in feature representation. In order …
Integration Of Spectral And Spatial Information Via Local Covariance Matrices For Segmentation And Classification Of Hyperspectral Images, Uğur Ergül, Gökhan Bi̇lgi̇n
Integration Of Spectral And Spatial Information Via Local Covariance Matrices For Segmentation And Classification Of Hyperspectral Images, Uğur Ergül, Gökhan Bi̇lgi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
In this work, a novel approach is presented for the feature extraction step in hyperspectral image processing to form more discriminative features between different pixel regions. The proposed method combines both spatial and spectral information, which is very important for segmentation and classification of hyperspectral images. For comparison, five different feature sets are formed using eigen decomposition of local covariance matrices of subcubes located around a pixel of interest in the scene. Subcubes of neighbor pixels are obtained by a windowed structure to expose pattern similarities. As a novel approach, local covariance matrices are computed in eigenspace and proposed feature …