Major advancements in kernel function approximation

Francis, Deena P.; Raimond, Kumudha

doi:10.1007/s10462-020-09880-z

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…Due to the use of the kernel trick ( Aizerman, 1964 ; Francis and Raimond, 2021 ), it is not possible to directly examine the relationships between the estimation and specific feature variables ( Üstün et al, 2007 ). In this instance, it was a necessity to use a kernel-based methodology as the data was both cross-sectional and in a time series, and the required output was a complex polynomial.…”

Section: Discussionmentioning

confidence: 99%

“…In this way, SVR aims to optimally minimise the distance from an estimated function output to the true output—in this case the collision rate by optimising the weight and bias coefficients of the input feature vectors. A radial-based kernel function was used to accommodate both a non-linear output response and a highly dimensional cross-sectional input ( Aizerman, 1964 ; Francis and Raimond, 2021 ). As we are using time-series data, it is highly likely that the current estimation will be similar to the previous estimation.…”

Section: Methodsmentioning

confidence: 99%

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Using skeletal position to estimate human error rates in telemanipulator operators

Piercy,

Herrmann,

Cangelosi

et al. 2024

Front. Robot. AI

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In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted in situ using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator’s skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Using skeletal position to estimate human error rates in telemanipulator operators

Piercy,

Herrmann,

Cangelosi

et al. 2024

Front. Robot. AI

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show abstract

“…There are two main approaches to large-scale kernel approximations and both can be applied to KGL. The former focuses on kernel matrix approximations using methods such as Nyström sampling [65], while the latter deals with the approximation of the kernel function itself, using methods such as Random Fourier Features [66]. Nonetheless, this paper focuses on the modelling perspective, and we will leave the algorithmic improvement as a future direction.…”

Section: Algorithm 1 Kernel Graph Learning (Kgl)mentioning

confidence: 99%

Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint

Pu,

Chau,

Dong

et al. 2020

Preprint

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“…Much lower runtime complexity could be achieved with the use of efficient linear models. As one of the key methods to kernelize algorithms with linear models, they are applied in various methods successfully, especially in machine learning [12].…”

Section: Introductionmentioning

confidence: 99%

Random Fourier Approximation of the Kernel Function in Programmable Networks

Guo

Chen

et al. 2022

Applied Mathematics and Nonlinear Sciences

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Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.

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Major advancements in kernel function approximation

Cited by 16 publications

References 29 publications

Using skeletal position to estimate human error rates in telemanipulator operators

Using skeletal position to estimate human error rates in telemanipulator operators

Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint

Random Fourier Approximation of the Kernel Function in Programmable Networks

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