Results 91 to 100 of about 415,263 (295)
Kernel methods in machine learning
, 2008 We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel.
Hofmann, Thomas +2 more
core +2 more sources
Advanced Materials, EarlyView.A fully flexible ion‐gel‐gated graphene‐channel transistor driven by a triboelectric nanogenerator enables self‐powered tactile sensing and synaptic learning. Mimicking spike‐rate‐dependent plasticity, the device exhibits frequency‐selective potentiation and depression, supporting rate‐coded neuromorphic computation even under flex.
Hanseong Cho +3 more
wiley +1 more source
Estimation of Weighted Extropy Under the α-Mixing Dependence Condition
StatsIntroduced as a complementary concept to Shannon entropy, extropy provides an alternative perspective for measuring uncertainty. While useful in areas such as reliability theory and scoring rules, extropy in its original form treats all outcomes equally,
Radhakumari Maya +3 more
doaj +1 more source
Nonparametric Estimation of Scalar Diffusion Processes of Interest Rates Using Asymmetric Kernels [PDF]
This paper proposes a nonparametric regression using asymmetric kernel functions for nonnegative, absolutely regular processes, and specializes this technique to estimating scalar diffusion models of spot interest rate.
Masayuki Hirukawa, Nikolay Gospodinov
core
Superionic Amorphous Li2ZrCl6 and Li2HfCl6
Advanced Materials, EarlyView.Amorphous Li2HfCl6 and L2ZrCl6 are shown to be promising solid‐state electrolytes with predicted ionic conductivities >20 mS·cm−1. Molecular dynamics simulations with machine‐learning force fields reveal that anion vibrations and flexible MCl6 octahedra soften the Li coordination cage and enhance mobility. Correlation between Li‐ion diffusivity and the
Shukai Yao, De‐en Jiang
wiley +1 more source
A Two-Stage Plug-In Bandwidth Selection and Its Implementation in Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation [PDF]
The performance of a kernel HAC estimator depends on the accuracy of the estimation of the normalized curvature, an unknown quantity in the optimal bandwidth represented as the spectral density and its derivative.
Hirukawa Masayuki
core
Advanced Materials, EarlyView.An overview of design principles and scalable fabrication strategies for multifunctional bio‐based packaging. Radiative cooling films, modified‐atmosphere films/membranes, active antimicrobial/antioxidant platforms, intelligent optical/electrochemical labels, and superhydrophobic surfaces are co‐engineered from material chemistry to mesoscale structure
Lei Zhang +6 more
wiley +1 more source
Unfolded deep kernel estimation-attention UNet-based retinal image segmentation. [PDF]
Sci Rep, 2023 Radha K +4 more
europepmc +1 more source
Advanced Materials, EarlyView.An active learning framework, grounded in independently generated in‐house experimental data, enables reliable discovery of high‐performance interfacial materials for perovskite solar cells. Iterative model refinement autonomously converges toward structurally robust quaternary ammonium architectures, establishing a new design principle for interfacial
Jongbeom Kim +8 more
wiley +1 more source
Nonparametric methods for volatility density estimation
, 2009 Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process.
Spreij, Peter +2 more
core