Results 71 to 80 of about 16,403 (208)
Advanced Quantum Technologies, EarlyView.The hybrid approach to Quantum Supervised Machine Learning is compatible with Noisy Intermediate Scale Quantum (NISQ) devices but hardly useful. Pure quantum kernels requiring faultātolerant quantum computers are more promising. Examples are kernels computed by means of the Quantum Fourier Transform (QFT) and kernels defined via the calculation of ...
Massimiliano Incudini +2 more
wiley +1 more source
Abstract and Applied Analysis, 2012 This paper investigates the numerical solution of nonlinear Fredholm-Volterra integro-differential equations using reproducing kernel Hilbert space method. The solution š¢(š„) is represented in the form of series in the reproducing kernel space.
Omar Abu Arqub +2 more
doaj +1 more source
Statistical Complexity of Quantum Learning
Advanced Quantum Technologies, EarlyView.The statistical performance of quantum learning is investigated as a function of the number of training data N$N$, and of the number of copies available for each quantum state in the training and testing data sets, respectively S$S$ and V$V$. Indeed, the biggest difference in quantum learning comes from the destructive nature of quantum measurements ...
Leonardo Banchi +3 more
wiley +1 more source
Numerical Solution of Seventh-Order Boundary Value Problems by a Novel Method
Abstract and Applied Analysis, 2014 We demonstrate the efficiency of reproducing kernel Hilbert space method on the seventh-order boundary value problems satisfying boundary conditions. These results have been compared with the results that are obtained by variational iteration method (VIM)
Mustafa Inc, Ali AkgĆ¼l
doaj +1 more source
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks [PDF]
, 2016 In this paper, we introduce a new image representation based on a multilayer kernel machine. Unlike traditional kernel methods where data representation is decoupled from the prediction task, we learn how to shape the kernel with supervision.
Mairal, Julien
core +3 more sources
Combining kernelised autoencoding and centroid prediction for dynamic multiāobjective optimisation
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multiāobjective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroidābased prediction (denoted by KAEP), for solving ...
Zhanglu Hou +4 more
wiley +1 more source
Full Details of Solving Initial Value Problems by Reproducing Kernel Hilbert Space Method [PDF]
, 2015 In this paper we solve in full details an initial value problem by reproducing kernel Hilbert space method and we notice that this solution is close to the exact solution.
AL- Azzawi, Saad N. +2 more
core +1 more source
International Statistical Review, EarlyView.Summary Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual distance covariance is bounded, but that its breakdown value is zero.
Sarah Leyder +2 more
wiley +1 more source
An accurate method for solving a singular second-order fractional Emden-Fowler problem
Advances in Difference Equations, 2018 In this paper, we study a singular second-order fractional Emden-Fowler problem. The reproducing kernel Hilbert space method (RKHSM) is employed to compute an approximation to the proposed problem.
Muhammed I Syam +3 more
doaj +1 more source
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
, 2017 Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series.
A Caponnetto +13 more
core +2 more sources