CAJ | 학술논문

对支持向量机这种功能强大的基于统计学习理论的机器学习方法进行了介绍,指出因其具有以任意精度逼近非线性方程和良好的泛化能力等优点,受到了越来越多的关注.同时,对近红外光谱技术进行了分析,并将支持向量机和近红外光谱相结合用于回归和模式识别,验证表明,将二者结合可取得满意的效果.
대지지향량궤저충공능강대적기우통계학습이론적궤기학습방법진행료개소,지출인기구유이임의정도핍근비선성방정화량호적범화능력등우점,수도료월래월다적관주.동시,대근홍외광보기술진행료분석,병장지지향량궤화근홍외광보상결합용우회귀화모식식별,험증표명,장이자결합가취득만의적효과.
Support vector machine (SVM) is a powerful machine learning technology based on statisticallearning theory (SLT). It has attracted growing research interest because of its obvious advantages such asnonlinear function approximation with arbitrary accuracy, and good generalization ability, unique and glob-ally optimal solutions. Near-infrared spectroscopy (NIR) analytical technique is simple, fast and of lowcost, making neither pollution nor damage to the samples, and can determine many components simultane-ously. In this paper, support vector machine (SVM) combined with NIR is applied to regression and pat-tern recognition, and satisfactory result is obtained