Results 11 to 20 of about 806,396 (338)
The Kernel Polynomial Method [PDF]
Reviews of Modern Physics, 2006 Efficient and stable algorithms for the calculation of spectral quantities and correlation functions are some of the key tools in computational condensed matter physics.
Alvermann, Andreas +3 more
core +4 more sources
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
, 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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A rescaled method for RBF approximation [PDF]
, 2016 In the recent paper [8], a new method to compute stable kernel-based interpolants has been presented. This \textit{rescaled interpolation} method combines the standard kernel interpolation with a properly defined rescaling operation, which smooths the ...
GE Fasshauer +7 more
core +3 more sources
Transfer Learning with Kernel Methods
Nature Communications, 2023 AbstractTransfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with ...
Adityanarayanan Radhakrishnan +3 more
openaire +4 more sources
A Kernel Method for Classification [PDF]
, 2004 Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale ...
MacDonald, Donald +4 more
openaire +3 more sources
Heat-kernel approach for scattering [PDF]
, 2015 An approach for solving scattering problems, based on two quantum field theory methods, the heat kernel method and the scattering spectral method, is constructed.
Dai, Wu-Sheng, Li, Wen-Du
core +2 more sources
Boosting as a kernel-based method [PDF]
Machine Learning, 2019 Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In the context of $\ell_2$ boosting, we start with a weak linear learner defined by a kernel $K$.
Aravkin A. Y. +2 more
openaire +3 more sources
A novel approach to attention mechanism using kernel functions: Kerformer
Frontiers in Neurorobotics, 2023 Artificial Intelligence (AI) is driving advancements across various fields by simulating and enhancing human intelligence. In Natural Language Processing (NLP), transformer models like the Kerformer, a linear transformer based on a kernel approach, have ...
Yao Gan +3 more
doaj +1 more source
IEEE Access, 2020 Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures.
Xuejun Zhao +3 more
doaj +1 more source