Parallel Clifford Support Vector Machines Using the Gaussian Kernel
Sign Up to like & getrecommendations! Published in 2017 at "Advances in Applied Clifford Algebras"
DOI: 10.1007/s00006-016-0726-2
Abstract: This work presents a parallelization method for the Clifford support vector machines, based in two characteristics of the Gaussian Kernel. The pure real-valued result and its commutativity allows us to separate the multivector data in… read more here.
Keywords: vector machines; gaussian kernel; clifford support; support vector ... See more keywords
Local linear regression with reciprocal inverse Gaussian kernel
Sign Up to like & getrecommendations! Published in 2019 at "Metrika"
DOI: 10.1007/s00184-019-00717-6
Abstract: In this paper, we propose a local linear estimator for the regression model $$Y=m(X)+\varepsilon $$Y=m(X)+ε based on the reciprocal inverse Gaussian kernel when the design variable is supported on $$(0,\infty )$$(0,∞). The conditional mean-squared error… read more here.
Keywords: regression; inverse gaussian; estimator; local linear ... See more keywords
DeCoST: unveiling cell type heterogeneity in spatial transcriptomics based on inter-domain alignment and Gaussian kernel conditional autoregressive
Sign Up to like & getrecommendations! Published in 2025 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbaf490
Abstract: Abstract Spatial transcriptomics (STs) has emerged as a transformative approach to elucidate cellular heterogeneity and spatial organization within complex tissue microenvironments. However, the analysis of ST data is challenged by limited spatial resolution, resulting in… read more here.
Keywords: spatial transcriptomics; heterogeneity spatial; decost; cell ... See more keywords
Knowledge Aided Covariance Matrix Estimation via Gaussian Kernel Function for Airborne SR-STAP
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2963838
Abstract: In practical airborne radar, the interference signals in training snapshots usually lead to inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), which seriously degrade radar performance and even occur target… read more here.
Keywords: interference signals; knowledge aided; stap; covariance matrix ... See more keywords
Second-Order Arnoldi Reduction Using Weighted Gaussian Kernel
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3167732
Abstract: Modeling and design of on-chip interconnect continue to be a fundamental roadblock for high-speed electronics. The continuous scaling of devices and on-chip interconnects generates self and mutual inductances, resulting in generating second-order dynamical systems. The… read more here.
Keywords: weighted gaussian; gaussian kernel; second order; reduction ... See more keywords
Gaussian-Kernel-Based Maximum Correntropy Kalman Filter With Adaptive Kernel Scale Selection
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3596048
Abstract: Dynamic systems often encounter disturbances like sensor outliers, which violate the Gaussian noise assumption in traditional Kalman filters (KFs). While maximum correntropy KFs (MCKFs) address this issue by utilizing higher order statistical information, their performance… read more here.
Keywords: kalman; kernel scale; maximum correntropy; selection ... See more keywords
Fuzzy and Crisp Gaussian Kernel-Based Co-Clustering With Automatic Width Computation
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2025.3546802
Abstract: Co-clustering algorithms separate a data matrix in blocks, by grouping, simultaneously, objects according to variables and variables according to objects, and has gained widespread attention in the last few years. At the same time, kernel-based… read more here.
Keywords: crisp gaussian; fuzzy crisp; kernel based; based clustering ... See more keywords
Gaussian Kernel Variance for an Adaptive Learning Method on Signals Over Graphs
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2022.3170652
Abstract: This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal values and… read more here.
Keywords: kernel; learning method; gaussian kernel; adaptive learning ... See more keywords
Nonlinear predistortion scheme based on Gaussian kernel-aided deep neural networks channel estimator for visible light communication system
Sign Up to like & getrecommendations! Published in 2019 at "Optical Engineering"
DOI: 10.1117/1.oe.58.11.116108
Abstract: Abstract. A scheme for Gaussian kernel-aided deep neural networks nonlinear predistortion (GK-DNNPD), which could effectively reduce the computational complexity of the receivers, is experimentally demonstrated. Compared with lookup table (LUT) PD, the GK-DNNPD could increase… read more here.
Keywords: deep neural; neural networks; kernel aided; nonlinear predistortion ... See more keywords
Learning Rates for Classification with Gaussian Kernels
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computation"
DOI: 10.1162/neco_a_00968
Abstract: This letter aims at refined error analysis for binary classification using support vector machine (SVM) with gaussian kernel and convex loss. Our first result shows that for some loss functions, such as the truncated quadratic… read more here.
Keywords: classification gaussian; learning rates; rates classification; svm gaussian ... See more keywords