Results 21 to 30 of about 176,617 (264)

Classification of raw milk composition and somatic cell count in water buffaloes with support vector machines [PDF]

Kafkas Universitesi Veteriner Fakültesi Dergisi, 2020
The study investigates the classification of milk quality with support vector machines (SVM) using the raw milk composition and somatic cell count (SCC) data on buffalos.
Yalcin TAHTALI
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Complexity Analysis of Primal-Dual Interior-Point Methods for Linear Optimization Based on a New Parametric Kernel Function with a Trigonometric Barrier Term

Abstract and Applied Analysis, 2014
We introduce a new parametric kernel function, which is a combination of the classic kernel function and a trigonometric barrier term, and present various properties of this new kernel function.
X. Z. Cai, G. Q. Wang, M. El Ghami, Y. J. Yue   +3 more
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Kekonvergenan MSE Penduga Kernel Seragam Fungsi Intensitas Proses Poisson Periodik dengan Tren Fungsi Pangkat

ComTech, 2012
The number of customers who come to a service center will be different for each particular time. However, it can be modeled by a stochastic process. One particular form of stochastic process with continuous time and discrete state space is a periodic ...
Ro’fah Nur Rachmawati
doaj   +1 more source

Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion

Advances in Mechanical Engineering, 2018
Aiming at the problem of gear fault diagnosis, in order to effectively extract the features and improve the accuracy of gear fault diagnosis, the method based on wavelet-packet independent component analysis and support vector machine with kernel ...
Lizheng Pan, Dashuai Zhu, Shigang She, Aiguo Song, Xianchuan Shi, Suolin Duan   +5 more
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A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Journal of Hydroinformatics, 2021
Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear.
Youliang Chen, Xiangjun Zhang, Hamed Karimian, Gang Xiao, Jinsong Huang   +4 more
doaj   +1 more source

Breakdown Point of Robust Support Vector Machines

Entropy, 2017
Support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has the serious drawback that it is sensitive to outliers in training samples.
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda   +2 more
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Reproducing kernel functions-based meshless method for variable order fractional advection-diffusion-reaction equations

Alexandria Engineering Journal, 2020
In this paper, on the basis of the reproducing kernel functions, a novel meshless algorithm is explored for fractional advection–diffusion-reaction equations (ADREs) with Caputo time variable order.
Xiuying Li, Boying Wu
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A novel method for identifying geomechanical parameters of rock masses based on a PSO and improved GPR hybrid algorithm

Scientific Reports, 2022
In view of the shortcomings of existing artificial neural network (ANN) and support vector regression (SVR) in the application of three-dimensional displacement back analysis, Gaussian process regression (GPR) algorithm is introduced to make up for the ...
Hanghang Yan, Kaiyun Liu, Chong Xu, Wenbo Zheng   +3 more
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An improved smoothed particle hydrodynamics approach using new inverse kernel function

Journal of Ocean Engineering and Science, 2022
The main limitation of Smoothed Particle Hydrodynamics (SPH) method that resists the method's potential is its lack of providing stability and accuracy to the numerical technique.
J.R. Rajapriyadharshini
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Feature Extraction of a Planetary Gearbox Based on the KPCA Dual-Kernel Function Optimized by the Swarm Intelligent Fusion Algorithm

Machines
The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction ...
Yan He, Linzheng Ye, Yao Liu
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