Results 31 to 40 of about 5,834,158 (292)
Energies, 2022 The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function–support vector regression and ...
Xiangbing Gao +3 more
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ComTech, 2012 The number of customers who come to a service center will be different for each particular time. However, it can be modeled by a stochastic process. One particular form of stochastic process with continuous time and discrete state space is a periodic ...
Ro’fah Nur Rachmawati
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Discrete Dynamics in Nature and Society, 2016 According to the characteristics that the kernel function of extreme learning machine (ELM) and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this ...
Senyue Zhang, Wenan Tan
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Abstract and Applied Analysis, 2014 We introduce a new parametric kernel function, which is a combination of the classic kernel function and a trigonometric barrier term, and present various properties of this new kernel function.
X. Z. Cai +3 more
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Breakdown Point of Robust Support Vector Machines
Entropy, 2017 Support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has the serious drawback that it is sensitive to outliers in training samples.
Takafumi Kanamori +2 more
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Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification. [PDF]
PLoS ONE, 2018 This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of Brain-Computer Interface (BCI).
Enzeng Dong +5 more
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Applying the possibilistic C-means algorithm in kernel-induced spaces [PDF]
, 2010 In this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the
Masulli, F. +5 more
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Science and Technology IndonesiaReproducing Kernel Hilbert Space (RKHS) is a Hilbert space consisting of functions that can be represented or reproduced by a kernel function. The development of data science has made RKHS a method that refers to an approach or technique using the ...
Bernadhita Herindri Samodera Utami +3 more
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Journal of Hydroinformatics, 2021 Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear.
Youliang Chen +4 more
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On the Approximation of Kernel functions
J. Mach. Learn. Res.Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selected based on characteristics of the problem and the data. This kernel is then employed to infer response variables at points, where no explanatory data were observed. The data considered here are located in
Paul Dommel, Alois Pichler
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