基于SVM-CoDOA的混合方法在医学诊断中的应用
Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach
摘要：
机器学习是人工智能的一个重要分支，通过有效的方法和技术来训练训练机器学习技术已经成为一项非常重要的任务。近年来，研究者们尝试使用替代技术来提高机器学习技术的能力。从解释，本研究的目的是介绍一种新的SVM-CoDOA（认知发展优化算法训练的支持向量机）系统的一般医疗诊断。详细地说，该系统由一个支持向量机，它是由CoDOA，一个新开发的优化算法训练。众所周知，优化算法的使用是训练和改进机器学习技术的基本任务。从这个意义上说，研究提供了一个面向医疗诊断的问题范围，以显示有效性的SVM-CoDOA混合形成。
英文摘要：
Machine Learning is an important sub-field of the Artificial Intelligence and it has been become a very critical task to train Machine Learning techniques via effective method or techniques. Recently, researchers try to use alternative techniques to improve ability of Machine Learning techniques. Moving from the explanations, objective of this study is to introduce a novel SVM-CoDOA (Cognitive Development Optimization Algorithm trained Support Vector Machines) system for general medical diagnosis. In detail, the system consists of a SVM, which is trained by CoDOA, a newly developed optimization algorithm. As it is known, use of optimization algorithms is an essential task to train and improve Machine Learning techniques. In this sense, the study has provided a medical diagnosis oriented problem scope in order to show effectiveness of the SVM-CoDOA hybrid formation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
