SVM在配方感官评估中的应用

Application on SVM in Formulating Sensory Evaluation

支持向量机(SVM)是近年来在统计学习理论的基础上发展起来的一种新的模式识别方法,在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势。本文从SVM的理论分析切入,阐述了SVM的基本原理、特性,提出用回归函数估计SVM进行建模来解决感官评估多类划分问题,并对其实际应用进行了算法选择、参数设计和实验验证。

Supprot Vector Machine （SVM） is a new pattern recognition method developed in recent years on the goundation of statistical learning theory.It wins populatity due to many attractive features and emphatical performance in the fields of nonlinear and high dimensional pattern recognition.The thesis begins from the theory analysis of SVM,expatiates its basic principles and characteristics.And the thesis proposes a method which is using SVM regression to construct the learning model for sensory evaluation,and takes a selection,design and testing for SVM algorithm and its parameters.