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Bergman Kernel, Szegö Kernel and Dirichlet Integral
Complex Analysis and Operator Theory, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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 +1 more
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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 +1 more
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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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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, 2010The 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 +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), 2015Sparse 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, 2011Many 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, 2002Principal 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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Bridging deep and multiple kernel learning: A review
Information Fusion, 2021Tinghua Wang, Wenyu Hu
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Contrastive Multi-View Kernel Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Jiyuan Liu, Xinwang Liu, Qing Liao
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