Results 211 to 220 of about 187,956 (260)

An Operator Analysis on Stochastic Differential Equation (SDE)-Based Diffusion Generative Models. [PDF]

Entropy (Basel)
Wu Y, Kawahara Y.
europepmc   +1 more source

Sustainable Valorization of Oil Palm Coproducts: Physicochemical Characterization and Potential Use in Insect Bioconversion. [PDF]

Foods
Almeida FC, Borges DPR, Santos LLCD, Oliveira JS, Ribeiro CVDM, Santos LFP, Ribeiro CDF, Cardoso LG, Assis DJ, Silva JBAD, Nascimento RQ, Ferreira ES, Ayena K, Umsza-Guez MA, Souza CO.   +14 more
europepmc   +1 more source

Reliability, bias, and computational cost of estimating the Bayes factor using bridge sampling and the Savage-Dickey density ratio. [PDF]

Behav Res Methods
Oberauer K, Musfeld P, Aust F.
europepmc   +1 more source

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Predictive Nyström method for kernel methods

Neurocomputing, 2017
Nystrm 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

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
openaire   +2 more sources

A Novel Kernel Method for Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
Kernel 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, 2015
The 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, 1972
The 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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