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Kernel Factory: An ensemble of kernel machines [PDF]

Expert Systems with Applications, 2013
We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by an automatically selected kernel function into a kernel matrix K.
M. BALLINGS, D. VAN DEN POEL
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Bergman Kernel, Szegö Kernel and Dirichlet Integral

Complex Analysis and Operator Theory, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Kernel Methods

2015
What the reader should know to understand this chapter • Notions of calculus. • Chapters 5, 6, and 7. • Although the reading of Appendix D is not mandatory, it represents an advantage for the chapter understanding.
CAMASTRA, Francesco, Vinciarelli, Alessandro   +1 more
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Kernel-Kohonen Networks

International Journal of Neural Systems, 2002
We investigate the combination of the Kohonen networks with the kernel methods in the context of classification. We use the idea of kernel functions to handle products of vectors of arbitrary dimension. We indicate how to build Kohonen networks with robust classification performance by transformation of the original data vectors into a possibly ...
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Semisupervised Kernel Matrix Learning by Kernel Propagation

IEEE Transactions on Neural Networks, 2010
The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency ...
Enliang, Hu, Songcan, Chen, Daoqiang, Zhang, Xuesong, Yin   +3 more
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Multiple Kernel Sparse Representation Based Gaussian Kernel and Power Kernel

2015 8th International Symposium on Computational Intelligence and Design (ISCID), 2015
Sparse representation classification (SRC) and kernel method have been successfully used in pattern recognition. On account of the limitations of the single kernel function, we proposed multiple kernel sparse classification method in face recognition to improve human face recognition rate.
Yanyong Zhu, Jiwen Dong, Hengjian Li
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Partitionable Kernels for Mapping Kernels

2011 IEEE 11th International Conference on Data Mining, 2011
Many of tree kernels in the literature are designed tanking advantage of the mapping kernel framework. The most important advantage of using this framework is that we have a strong theorem to examine positive definiteness of the resulting tree kernels.
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Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, 2002
Principal Component A nalysis and Fisher Linear Discriminant methods have demonstrated their success in fac edete ction, r ecognition and tr acking. The representations in these subspace methods are based on second order statistics of the image set, and do not address higher order statistical dependencies such as the relationships among three or more ...
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kernelled | kerneled, adj.¹

2023
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Simultaneous Global and Local Graph Structure Preserving for Multiple Kernel Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2021
Zhenwen Ren, Quansen Sun
exaly