SMO:序列最小优化
Sequential Minimal Optimization(SMO)是一种常用于机器学习分类任务的高效优化算法,尤其在支持向量机(SVM)的训练过程中广泛应用。为了方便书写和口头交流,该术语常被缩写为SMO。其标准中文译名为“序列最小优化”,这一方法通过分解大规模优化问题为多个小规模子问题,显著提升了计算效率,在软件开发和数据科学领域具有重要价值。
Sequential Minimal Optimization具体释义
Sequential Minimal Optimization的英文发音
例句
- An Improved Working Set Selection Strategy for Sequential Minimal Optimization(SMO) Algorithm
- 改进工作集选择策略的序贯最小优化算法
- Comparing three train algorithms, choice Platt's Sequential Minimal Optimization(SMO) algorithm.
- 对三种训练算法优缺点进行了比较,最后选择了Platt提出的序列最小最优化算法。
- The fast training algorithm of the method is also given by contrast with standard sequential minimal optimization.
- 通过对比标准序列最小优化(SMO)方法,得到快速训练算法。
- This paper mainly studied the sequential minimal optimization algorithm of support vector machine in statistical learning theory. The support vector machine composed by least sequence algorithm has been used in serial number identification.
- 本文实现了人民币图像预处理和序列号识别,主要研究了统计学习理论中支持向量机的次序最小优化算法,并将其构建的支持向量机用于序列号识别。
- SVM forecast models are established for every point of 24, point loads, and an improved sequential minimal optimization method is used to train SVM.
- 对24点每点建立一个SVM预测模型,采用改进的序列极小优化算法实现对SVM的快速训练。
本站英语缩略词为个人收集整理,可供非商业用途的复制、使用及分享,但严禁任何形式的采集或批量盗用
若SMO词条信息存在错误、不当之处或涉及侵权,请及时联系我们处理:675289112@qq.com。