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Feature based camera model identification plays an important role for forensics investigations on images. The conventional feature based identification schemes suffer from the problem of unknown models, that is, some images are captured by the camera models previously unknown to the identification system. To address this problem, we propose a new scheme: Source Camera Identification with Unknown models...
Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task. A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here. The powerful framework of Harmony Search has been utilized...
This paper proposes a passive islanding detection technique for distributed generations in grid-connected microgrids and presents a comprehensive comparative analysis of intelligent classifiers for passive islanding detection application. The proposed method utilizes pattern recognition techniques in classification of underlying signatures of wide variety of system events on critical system parameters...
Diagnosis of disease is done by physical examination of patient by physician. For internal observation physician requires help of sonography, MRI, pathological tests reports etc. In Ayurveda Nadi-Pariksha (pulse examination) is used for making the diagnosis. It uses pulse signal sensed at radial artery on wrist below the thumb for diagnosis manually. The pulse signal contains very useful information...
This paper proposes a multiple kernel construction method for kernel discriminant analysis. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC), we present an iterative scheme for weight optimization. The experiments on several UCI real data benchmarks show that, the constructed kernel with...
Many studies [1]–[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of pattern recognition and machine learning, many researches [3]–[5] show that kernel method is computationally efficient,...
In remote sensing researches, the curse of dimensionality is one greatly difficult classification problem. Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), can alleviate small sample size and high dimensionality concern and obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. A dynamic...
In this paper, we propose a max modular support vector machine (M2-SVM) and its two variations for pattern classification. The basic idea behind these methods is to decompose training samples of one class into several parts and learn each part by one modular classifier independently. To implement these methods, a dasiapart-against-otherspsila training strategy and a max modular combination principle...
In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability...
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