scholarly journals A dual coordinate descent method for large-scale linear SVM

2008 ◽  
Author(s):  
Cho-Jui Hsieh ◽  
Kai-Wei Chang ◽  
Chih-Jen Lin ◽  
S. Sathiya Keerthi ◽  
S. Sundararajan
Keyword(s):  
Large Scale ◽  
Descent Method ◽  
Coordinate Descent ◽  
Coordinate Descent Method ◽  
Dual Coordinate Descent ◽  
Linear Svm

scholarly journals Calculation of elements of machine-building structures on vibration strength

2021 ◽  
Author(s):  
Andrii Babenko ◽  
Oleg Boronko ◽  
Serhiy Trubachev ◽  
Yaroslav Lavrenko
Keyword(s):  
Large Scale ◽  
Descent Method ◽  
Machine Building ◽  
Coordinate Descent ◽  
Automated System ◽  
Engineering Practice ◽  
Building Structures ◽  
Coordinate Descent Method ◽  
Vibration Strength ◽  
Calculation Algorithms

A method and an automated system for calculating structural elements for vibration strength have been developed. The calculation algorithms are based on a new method of forming Rayleigh-type functionals and minimizing them by the coordinate descent method. The use of the coordinate descent method avoids the problems associated with the formation, storage and operation of global matrices of stiffness and mass. This makes it possible to solve large-scale problems using only the operational memory of the PC. The developed approach allows to solve problems on own and forced fluctuations. The software is formed on a modular basis, which allows you to improve and expand this package of applications. The software has been tested on a large number of test and application tasks. The proposed calculation method and the developed automated system were implemented in engineering practice.

scholarly journals Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mingzhu Tang ◽  
Chunhua Yang ◽  
Kang Zhang ◽  
Qiyue Xie
Keyword(s):  
Support Vector Machine ◽  
Computational Cost ◽  
Imbalanced Data ◽  
Descent Method ◽  
Coordinate Descent ◽  
Support Vector ◽  
Coordinate Descent Method ◽  
Imbalanced Data Classification ◽  
Positive Class ◽  
Dual Coordinate Descent

Cost-sensitive support vector machine is one of the most popular tools to deal with class-imbalanced problem such as fault diagnosis. However, such data appear with a huge number of examples as well as features. Aiming at class-imbalanced problem on big data, a cost-sensitive support vector machine using randomized dual coordinate descent method (CSVM-RDCD) is proposed in this paper. The solution of concerned subproblem at each iteration is derived in closed form and the computational cost is decreased through the accelerating strategy and cheap computation. The four constrained conditions of CSVM-RDCD are derived. Experimental results illustrate that the proposed method increases recognition rates of positive class and reduces average misclassification costs on real big class-imbalanced data.

Coordinate Descent Method for k-means

2021 ◽  
pp. 1-1
Author(s):  
Feiping Nie ◽  
Jingjing Xue ◽  
Danyang Wu ◽  
Rong Wang ◽  
Hui Li ◽  
...  
Keyword(s):  
Descent Method ◽  
Coordinate Descent ◽  
Coordinate Descent Method
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