Results 261 to 270 of about 248,031 (304)
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Predictive Nyström method for kernel methods
Neurocomputing, 2017Nystrm method is a widely used matrix approximation method for scaling up kernel methods, and existing sampling strategies for Nystrm method are proposed to improve the matrix approximation accuracy, but leaving approximation independent of learning, which can result in poor predictive performance of kernel methods.
Lizhong Ding, Shizhong Liao
exaly +2 more sources
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
openaire +2 more sources
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Kernel Methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, we present a kernel method for clustering inspired by the classical K-Means algorithm in which each cluster is iteratively ...
CAMASTRA F, VERRI, ALESSANDRO
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Eigenvalues Ratio for Kernel Selection of Kernel Methods
Proceedings of the AAAI Conference on Artificial Intelligence, 2015The selection of kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. Existing kernel selection approaches commonly use some measures of generalization error, which are usually difficult to estimate and have slow convergence rates.
Yong Liu 0018, Shizhong Liao
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The Reproducing Kernel Method. II
Journal of Mathematical Physics, 1972The explicit solution of the Cauchy problem ∂N/∂t = HN by means of reproducing kernels is obtained under various forms: conformal mapping expansions, Sheffer polynomial expansion, polynomials orthogonal on a family of curves; the convergence is studied for both Szegö and Bergman kernels.
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Kernel methods for melanoma recognition.
Studies in health technology and informatics, 2006Skin cancer is a spreading disease in the western world. Early detection and treatment are crucial for improving the patient survival rate. In this paper we present two algorithms for computer assisted diagnosis of melanomas. The first is the support vector machines algorithm, a state-of-the-art large margin classifier, which has shown remarkable ...
La Torre, Elisabetta +3 more
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2006
Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering.
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Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering.
openaire +2 more sources
2007
During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques.
Cristianini, N. +2 more
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During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques.
Cristianini, N. +2 more
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Kernel-factorization deconvolution method
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990zbMATH Open Web Interface contents unavailable due to conflicting licenses.
F. N. Kong, Z. P. Li
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Decomposition Method for Tree Kernels
2007We often meet the tree decomposition task in the tree kernel computing. And tree decomposition tends to vary under different tree mapping constraint. In this paper, we first introduce the general tree decomposition function, and compare the three variants of the function corresponding to different tree mapping.
Peng Huang, Jie Zhu
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