摘要
SVM是一种有坚实理论基础的新颖的小样本学习方法。它基本上不涉及概率测度及大数定律等,因此不同于现有的统计方法。SVM在解决小样本、非线性及高维模式识别问题中具有许多特有的优势,并能够推广应用到函数拟合等其他机器学习问题中。目前SVM已广泛应用各个领域,但其也有一定的局限性。本文综述了最近几年来支持向量机在临床医学中的应用状况,并探讨其应用前景。
The support vector machine is a theory which have a solid foundation of the novel method of studying small samples. Basically, it does not involve probability measure and the law of large numbers, and so on. It has difference from the existing statistical methods. The support vector machine in solving the small sample, non- linear and high dimensional pattern recognition problem has a lot of unique advantages, and be able to promote the use of the function fitting, and other machine learning problems. Support vector machine has been widely applied in various fields, but it also has some limitations. This review summarizes support vector machine applications in clinical medicine in the most recent years and then explores its application prospects.
出处
《海南医学》
CAS
2009年第9期134-137,共4页
Hainan Medical Journal
关键词
支持向量机
临床医学
Support vector machine
Clinical medicine
