After the mean center preprocessing, CNN, SVM and LDA were used to model and analyze.

经过均值中心预处理后，采用CNN、SVM和LDA进行建模与分析。

In addition, the RGB-DSIFT-LLC features were input into a linear support vector machine (SVM) classifier for identifying the maturity of fruits.

此外，RGB-DSIFT-LLC特征被输入到一个线性支持向量机（SVM）分类器中，用于识别水果的成熟度。

With 5-fold cross validation, results showed that the best average classification accuracy across the four SCA classes was 85.0% with the modified OVO-SVM algorithm.

通过5折交叉验证，结果显示，在种SCA类别中，改进的OVO-SVM算法达到了最佳的平均分类准确率，高达85.0%。

In three-group classification, the SVM learner achieved 90.8 % accuracy, while in two-group classification (healthy vs symptomatic HLB leaves), the accuracy reached to 96%.

在三组分类中，支持向量机学习器达到了90.8%的准确率，而在两组分类（健康与染病黄龙病叶片）中，准确率则提升至96%。

The SCA quantification accuracies achieved in this study using the SVM algorithm showed the promise of using machine learning algorithms in this case of aphid density estimation on sorghum leaves.

本研究利用SVM算法实现的SCA量化准确性，展示了在估算高粱叶上蚜虫密度这一场景中应用机器学习算法的潜力。

Several machine learning algorithms, such as support vector machine (SVM), k-nearest neighbour (kNN), logistic regression (LR), naive Bayes and ensemble learning, were compared to model the healthy and HLB-infected samples after parameter optimization.

比较了几种机器学习算法，如支持向量机（SVM）、k近邻算法（kNN）、逻辑回归（LR）、朴素贝叶斯以及集成学习，在参数优化后对健康和黄龙病感染样本进行建模。

In the application of simultaneous identification of gender and variety, CNN model has the highest accuracy of 94%, LDA model has the medium accuracy of 92.5%, and SVM model has the lowest accuracy of 89.5%.

在性别与品种同时识别的应用中，卷积神经网络（CNN）模型准确率最高，达到94%，线性判别分析（LDA）模型居中，准确率为92.5%，而支持向量机（SVM）模型准确率最低，为89.5%。