Results 241 to 250 of about 33,561,195 (290)

Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method

Neurocomputing, 2019
Rolling element bearings are widely used in rotating machines and their faults can lead to heavy investment and productivity losses so that the fault diagnosis of bearing is very important for guaranteeing a high performance transmission.
Zenghui An, Shunming Li, Jinrui Wang, Yu Xin, Kun Xu   +4 more
semanticscholar   +1 more source

Numerical simulation of time-fractional partial differential equations arising in fluid flows via reproducing Kernel method

International journal of numerical methods for heat & fluid flow, 2019
Purpose The subject of the fractional calculus theory has gained considerable popularity and importance due to their attractive applications in widespread fields of physics and engineering.
O. A. Arqub
semanticscholar   +1 more source

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

Coupling privileged kernel method for multi-view learning

Information Sciences, 2019
Multi-view learning concentrates on fully using the data collected from diverse domains or obtained from various feature extractors to learn effectively.
Jingjing Tang, Ying-jie Tian, Dalian Liu, Gang Kou   +3 more
semanticscholar   +1 more source

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
openaire   +4 more sources

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
openaire   +1 more source

Contrastive Multi-View Kernel Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views ...
Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Qing Liao, Yuanqing Xia   +4 more
semanticscholar   +1 more source

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.
openaire   +3 more sources

Kernel methods for melanoma recognition.

Studies in health technology and informatics, 2006
Skin 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, TOMMASI, TATIANA, CAPUTO, BARBARA, GIGANTE, Giovanni Ettore   +3 more
openaire   +3 more sources

Kernel Methods for Clustering

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.
openaire   +2 more sources