Feature detection from local energy
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Independent component analysis: algorithms and applications
Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
No-reference image quality assessment based on DCT domain statistics
Signal Processing
A natural image quality evaluation metric
Signal Processing
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image quality assessment based on multiscale geometric analysis
IEEE Transactions on Image Processing
Reduced-reference IQA in contourlet domain
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel Adaptive Filtering: A Comprehensive Introduction
Kernel Adaptive Filtering: A Comprehensive Introduction
No-reference image quality assessment using structural activity
Signal Processing
A variable step size LMS algorithm
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
The Kernel Least-Mean-Square Algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
The least mean fourth (LMF) adaptive algorithm and its family
IEEE Transactions on Information Theory
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Blind Image Quality Assessment Using a General Regression Neural Network
IEEE Transactions on Neural Networks
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
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The Kernel method is a powerful tool for extending an algorithm from linear to nonlinear case. Metalearning algorithm learns the base learning algorithm, thus to improve performance of the learning system. Usually, metalearning algorithms exhibit faster convergence rate and lower Mean-Square Error (MSE) than the corresponding base learning algorithms. In this paper, we present a kernelized metalearning algorithm, named KIMEL, which is a metalearning algorithm in the Reproducing Kernel Hilbert Space (RKHS). The convergence analyses of the KIMEL algorithm are performed in detail. To demonstrate the effectiveness and advantage of the proposed algorithm, we firstly apply the algorithm to a simple example of nonlinear channel equalization. Then we focus on a more practical application in blind Image Quality Assessment (IQA). Experimental results show that the KIMEL algorithm has superior performance.