There are also non-tree-based approaches, like Support Vector Machines, which essentially slice up the decision space using arbitrary lines.

还有一些非基于树的方法，例如支持向量机，它本质上是使用任意线来分割决策空间。

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

，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。

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（健病黄龙病叶片）中，准确率则提升至96%。

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，在参数优化后对健和黄龙病感样本进行建模。

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%。

Se veral machine learning algorithms (logistic regression, decision tree, support vector machine, k-nearest neighbor, linear discri minant analysis, and ensemble learning) were used to discriminate between disease groups: healthy, symptomatic HLB-infecte d, and asymptomatic HLB-infected, based on leaf reflectance.

采用了多种机器学习算法（逻辑回归、决策树、支持向量机、K近邻、线性判别分析及集成学习），基于叶片反射率对疾病群体——健、症状性黄龙病感和无症状性黄龙病感——进行区分。