A note on kernel methods for multiscale systems with critical transitions
Sign Up to like & getrecommendations! Published in 2018 at "Mathematical Methods in the Applied Sciences"
DOI: 10.1002/mma.5394
Abstract: We study the maximum mean discrepancy (MMD) in the context of critical transitions modelled by fast-slow stochastic dynamical systems. We establish a new link between the dynamical theory of critical transitions with the statistical aspects… read more here.
Keywords: systems critical; kernel methods; methods multiscale; note kernel ... See more keywords
Spatial Analysis Made Easy with Linear Regression and Kernels
Sign Up to like & getrecommendations! Published in 2019 at "Epidemics"
DOI: 10.1016/j.epidem.2019.100362
Abstract: Kernel methods are a popular technique for extending linear models to handle non-linear spatial problems via a mapping to an implicit, high-dimensional feature space. While kernel methods are computationally cheaper than an explicit feature mapping,… read more here.
Keywords: regression; easy linear; kernel methods; spatial analysis ... See more keywords
Predictive Nyström method for kernel methods
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.12.047
Abstract: 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,… read more here.
Keywords: kernel methods; approximation; method; nystrm method ... See more keywords
Kernel Methods for Predicting Yields of Chemical Reactions.
Sign Up to like & getrecommendations! Published in 2021 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.1c00699
Abstract: The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used… read more here.
Keywords: quantum chemical; kernel methods; methods predicting; structure based ... See more keywords
Fisher kernels match deep models
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DOI: 10.1049/el.2016.3320
Abstract: Deep models have recently shown improved performance on numerous benchmark tasks in computer vision and machine learning. The availability of huge amount of digital data, possibility of massively parallel computations on graphics processing units and… read more here.
Keywords: kernel methods; deep models; kernels match; match deep ... See more keywords
Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2016.2565683
Abstract: Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without… read more here.
Keywords: implementing kernel; kernel methods; projection trick; nonlinear projection ... See more keywords
Scaling Up Generalized Kernel Methods.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3059702
Abstract: Kernel methods have achieved tremendous success in the past two decades. In the current big data era, data collection has grown tremendously. However, existing kernel methods are not scalable enough both at the training and… read more here.
Keywords: generalized kernel; scaling generalized; kernel methods;