i need paraphrase to this text

A support-vector machine constructs area or set of areas in high or dimensional space, which can be used for classification to classify the data. A good separation is achieved by the area that has with the largest distance to the nearest training-data point of any class. S, since in general the larger the margin, the lower the generalization error of the classifier (supervised learning)

• Accuracy.
• SVM are very good when there is no idea about the data.
• It scales relatively well to high dimensional data.
• SVM models have generalization in practice; the risk of over-fitting is less in SVM.
• It works well with even unstructured and semi-structured data like text, images, and trees [8].

• Choosing a good kernel function is not easy.
• Long training time for large datasets.
• Difficult to understand and interpret the final model, variable weights and individual impact [8].