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Matrix-pattern-oriented Ho-Kashyap classifier with regularization learning
Existing classifier designs generally base on vector pattern, hence, when a non-vector pattern such as a face image as the input to the classifier, it has to be first concatenated to a vector. InExpand
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MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each generated feature spaceExpand
  • 107
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Pattern Representation in Feature Extraction and Classifier Design: Matrix Versus Vector
The matrix, as an extended pattern representation to the vector, has proven to be effective in feature extraction. However, the subsequent classifier following the matrix-pattern-oriented featureExpand
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New Least Squares Support Vector Machines Based on Matrix Patterns
  • Z. Wang, S. Chen
  • Computer Science, Mathematics
  • Neural Processing Letters
  • 1 August 2007
Support vector machine (SVM), as an effective method in classification problems, tries to find the optimal hyperplane that maximizes the margin between two classes and can be obtained by solving aExpand
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Entropy-based fuzzy support vector machine for imbalanced datasets
Abstract Imbalanced problem occurs when the size of the positive class is much smaller than that of the negative one. Positive class usually refers to the main interest of the classification task.Expand
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Gravitational fixed radius nearest neighbor for imbalanced problem
We use the gravitational scenario into the fixed radius nearest neighbor rule.The proposed GFRNN deals with imbalanced classification problem.GFRNN does not need any manual parameter setting orExpand
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A simplified multi-class support vector machine with reduced dual optimization
Support vector machine (SVM) was initially designed for binary classification. To extend SVM to the multi-class scenario, a number of classification models were proposed such as the one by CrammerExpand
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Multiple empirical kernel learning with locality preserving constraint
Abstract Multiple Kernel Learning (MKL) is flexible in dealing with problems involving multiple and heterogeneous data sources. However, the necessity of inner-product form restricts its applicationExpand
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Regularized Matrix-Pattern-Oriented Classification Machine with Universum
Regularization has the ability to effectively improve the generalization performance, which is due to its control for model complexity via priori knowledge. Matrixized learning as one kind ofExpand
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Phase Transition Behavior and Molecular Structures of Monounsaturated Phosphatidylcholines
High resolution differential scanning calorimetric studies were performed to investigate the thermotropic phase behavior of 26 molecular species of sn-1 saturated/sn-2 monounsaturatedExpand
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