Support Vector Machine (SVM, 支持向量机)

发明者: Vapinik [苏]

Linear support vector machine(LSVM) in the linearly separable case and Hard Margin Maximization.

LSVM in the linearly separable case suppose a training dataset in a feature space, is given \(\(T = \{(x_1. y_1), (x_2, y_2), ..., (x_n, y_n)\}\)\) where \(x_i \in X\) = \(R^n\), \(y_i \in Y = \{+1, -1\}\), \(i=1,2,...,N\).

Definition (linear separability of dataset) Given T, if there is a hyper plane (超平面) \(\(S: \omega \cdot X + b = 0\)\) Than can precisely divide the positive instance points and negative instance points of dataset into both sides of the hyper plane.

i.e. For all instances \(i\) with \(y_i=+1\), \(W \cdot x + b > 0\) and for all instances with \(y_i\)=-1, \(\omega \cdot x + b < 0\). Then the dataset T is called a linearly separate data.

^
|   \   o
|    \     o
| x   \  o
|  x   \   o
| x  x  \
+---------------->

- o indicates positive instance points. - x indicates negative instance points.

Find: 1. \(\omega^* \cdot x + b^* = 0\) 2. \(f(x) = sign(\omega^* \cdot x + b^*)\)

Function margin and Geometric margin 1. Function margin: with regard to the give training dataset T and the hyperplane(\(W\), \(b\)). Define the functional margin of hyperplane on sample point \((x_i, y_i)\) as \(\(\hat{\gamma}_i = y_i(\omega \cdot x_i + b)\)\) \(\(\hat{\gamma} = \min_{i=0,...,N}\hat{\gamma}_i\)\) 2. Two facts 1. \((\omega^*, b^*)\) 与 \((a\omega^*, ab^*)\) 相同. 2. Distance from \((x_0, y_0)\) to the plane \(\omega_1x + \omega_2y + b = 0\): \(\(d = \frac{|\omega_1x + \omega_2y + b|}{\sqrt{\omega_1^2 + \omega_2^2}} = \frac{|\omega_1x + \omega_2y + b|}{||\omega||}\)\) Vector \(\vec{x}_0\) to the hyperplane \(\omega_1x + \omega_2y + b = 0\): \(\(d = \frac{|\omega^* \cdot x_0 + b^*|}{||\omega^*||}\)\)

Support Vector Machine (SVM, 支持向量机)

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