Support vector machine
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https://en.wikipedia.org/wiki/Support_vector_machine
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.
以上是关于Support vector machine的主要内容,如果未能解决你的问题,请参考以下文章
支持向量机(support vector machines, SVM)
机器学习技法:06 Support Vector Regression
2.机器学习技法- Dual Support Vector Machine