Results 1 to 10 of about 182,715 (258)
, 2023 This chapter introduces a powerful class of machine learning approaches called kernel methods, which present an alternative to arguably more widely known neural network approaches. Kernel methods can learn even highly nonlinear problems by making an implicit transformation from a low-dimensional input space into a higher-dimensional feature space. This
Pinheiro Jr, Max, Dral, Pavlo
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Biologically-Inspired Pulse Signal Processing for Intelligence at the Edge
Frontiers in Artificial Intelligence, 2021 There is an ever-growing mismatch between the proliferation of data-intensive, power-hungry deep learning solutions in the machine learning (ML) community and the need for agile, portable solutions in resource-constrained devices, particularly for ...
Kan Li, José C. Príncipe
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Unlabeled $(2+2)$-free posets, ascent sequences and pattern avoiding permutations [PDF]
Discrete Mathematics & Theoretical Computer Science, 2009 We present statistic-preserving bijections between four classes of combinatorial objects. Two of them, the class of unlabeled $(\textrm{2+2})$-free posets and a certain class of chord diagrams (or involutions), already appeared in the literature, but ...
Mireille Bousquet-Mélou +3 more
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Wilf classification of triples of 4-letter patterns I [PDF]
Discrete Mathematics & Theoretical Computer Science, 2017 This paper is first part of a complete paper in arXiv , see 1605.04969.
David Callan +2 more
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Mathematics, 2022 In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform.
Hanen Chaouch +4 more
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Nonparametric Estimation of the Expected Shortfall Regression for Quasi-Associated Functional Data
Mathematics, 2022 In this paper, we study the nonparametric estimation of the expected shortfall regression when the exogenous observation is functional. The constructed estimator is obtained by combining the double kernels estimator of both conditional value at risk and ...
Larbi Ait-Hennani +3 more
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Scalar-on-Function Relative Error Regression for Weak Dependent Case
Axioms, 2023 Analyzing the co-variability between the Hilbert regressor and the scalar output variable is crucial in functional statistics. In this contribution, the kernel smoothing of the Relative Error Regression (RE-regression) is used to resolve this problem ...
Zouaoui Chikr Elmezouar +5 more
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development.
Shasha Yuan +6 more
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Instrumental variable regression via kernel maximum moment loss
Journal of Causal Inference, 2023 We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction known as a maximum moment restriction (MMR).
Zhang Rui +3 more
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Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
Applied Sciences, 2023 Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated ...
Qing Zou +3 more
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