one-class svm

One-class SVM (Support Vector Machine) is a type of machine learning algorithm used for anomaly detection. It is designed to find a hyperplane that separates the normal data points from the outliers in a dataset. The algorithm is trained on a set of data points that are considered normal, and then it is used to classify new data points as either normal or anomalous. One-class SVM is particularly useful when there is only one class of data available for training.

相关问题

One-class SVM流程图

One-class SVM的流程大致如下:
1. 选取训练集中的正样本进行模型训练。
2. 选取合适的核函数并估计其参数。
3. 通过解决对偶问题得到支持向量。
4. 使用得到的支持向量进行预测，并利用预测结果进行异常点检测。

one-class SVM 流程图

one-class SVM 的流程图如下:
1. 首先, 从训练数据集中提取出正类样本.
2. 使用这些样本训练一个 SVM 模型.
3. 使用训练出的模型对新的未知样本进行分类, 如果样本在模型分界面的一侧, 则判定为正类, 反之则判定为负类.