Robotics & Machine Learning Daily News2024,Issue(Jun.13) :44-44.

Data on Support Vector Machines Reported by Researchers at Chongqing Normal Univ ersity (Sparse L 0-norm Least Squares Support Vector Machine With Feature Selection)

重庆师范大学支持向量机研究报告(带特征选择的稀疏L 0范数最小二乘支持向量机)

Robotics & Machine Learning Daily News2024,Issue(Jun.13) :44-44.

Data on Support Vector Machines Reported by Researchers at Chongqing Normal Univ ersity (Sparse L 0-norm Least Squares Support Vector Machine With Feature Selection)

重庆师范大学支持向量机研究报告(带特征选择的稀疏L 0范数最小二乘支持向量机)

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摘要

由一名新闻记者-机器人与机器学习每日新闻编辑-调查人员发布了关于支持向量机的新报告。根据NewsRx记者在重庆的新闻报道,研究表明:“最小二乘支持向量机(LSSVM)是一种基于Hyperp车道的强大分类工具,但经典的LSSVM由于缺乏特征选择能力,在小样本量数据S ETS(SSS)上表现不佳。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Su pport Vector Machines. According to news reporting originating in Chongqing, Peo ple's Republic of China, by NewsRx journalists, research stated, “Least squares support vector machine (LSSVM) is a powerful classification tool based on hyperp lanes. But the classical LSSVM does not perform well on small sample size data s ets (SSS) because it lacks feature selection capability.”

Key words

Chongqing/People's Republic of China/Asia/Emerging Technologies/Machine Learning/Support Vector Machines/Vector Ma chines/Chongqing Normal University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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