SVM学习

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 for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into 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 on.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel
trick, implicitly mapping their inputs into high-dimensional feature spaces.

When data are not labeled, supervised learning is not possible, and an unsupervised learning approach is required, which attempts to find natural clustering of the data to groups, and then map new data to these formed groups. The clustering algorithm which
provides an improvement to the support vector machines is called support vector clustering[2] and
is often used in industrial applications either when data is not labeled or when only some data is labeled as a preprocessing for a classification pass.