Results 311 to 320 of about 29,026,283 (376)
Some of the next articles are maybe not open access.
Kernel method for overlapping coefficients estimation
Communications in statistics. Simulation and computation, 2020Most studies of estimating overlapping coefficients assume two specific parametric models for population densities. The methods that used such of this assumption are called parametric methods, which work well when these model assumptions are valid ...
Omar M. Eidous +1 more
semanticscholar +1 more source
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
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
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
openaire +5 more sources
A kernel method for multi-labelled classification
Neural Information Processing Systems, 2001This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of such a system can be weak [5, 7].
A. Elisseeff, J. Weston
semanticscholar +1 more source
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
openaire +1 more source
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 +1 more source
Kernel-Based Methods Kernel@Kernel-based method [PDF]
, 2010 Inspired by the success of support vector machines, to improve generalization and classification abilities, conventional pattern classification techniques have been extended to incorporate maximizing margins and mapping to a feature space. For example, perceptron algorithms [1–4], neural networks (Chapter 9), and fuzzy systems (Chapter 10) have ...
openaire +1 more source
2008
Kernel methods (Cristianini and Shawe-Taylor 2000; Herbrich 2002; Scholkopf and Smola 2002; Shawe-Taylor and Cristianini 2004) can be regarded as machine learning techniques which are “kernelised” versions of other fundamental machine learning methods.
Chalup, Stephan, Mitschele, Andreas
openaire +2 more sources
Kernel methods (Cristianini and Shawe-Taylor 2000; Herbrich 2002; Scholkopf and Smola 2002; Shawe-Taylor and Cristianini 2004) can be regarded as machine learning techniques which are “kernelised” versions of other fundamental machine learning methods.
Chalup, Stephan, Mitschele, Andreas
openaire +2 more sources
A tutorial on kernel methods for categorization [PDF]
Journal of Mathematical Psychology, 2007 The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial ...
Jäkel, F., Schölkopf, B., Wichmann, F.
openaire +2 more sources
, 1996 The goal of the heat kernel method is to express (2.40) as an integral over the fixed point set M γ in M of the transformation γ. Here M γ = M if γ is the identity. The method is based on the following observations about arbitrary elliptic differential operators D, acting on sections of a smooth vector bundle F over a compact manifold M, which admits a
openaire +1 more source
Journal of machine learning research, 2012
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions.
A. Gretton +4 more
semanticscholar +1 more source
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions.
A. Gretton +4 more
semanticscholar +1 more source