com豆丁网 贝叶斯（Naïve Bayes, NB）、K 近邻（K nearest neighbor，K-NN）和线性最小二乘法拟合（Linear Least Squares Fit， LLSF）等五种代表性分类器的性能进行了对比，在准确率、召回率、F1、错误率(分别在宏平均、微平 均下测试)等..
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用线性最小二乘法、迭代法以及二分法与最小二乘法相结合的方法，以积分方程、微分方程和放热速率方程拟合dsc数据。
The DSC data obtained are fitted to the integral, differential and exothermic rate equations by linear least-squares, iterative, combined dichotomous and least-squares methods, respectively.
经实验验证和测试表明，分辨力达5位，最小二乘法拟合线性度优于0.005%。
Verifying test indicates that its resolution is better than 5 digits, and linearity referenced to Method of Least Square better than 0.005%.
该法用线性最小二乘法、迭代法以及二分法与最小二乘法相结合的方法，以积分方程、微分方程和放热速率方程拟合实验数据。
Thedata are fitted to the integral, differential and exothermic rate equations by linear least-squares, iterative, combined dichotomous and least-squares methods, respectively.
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