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Machine Learning: Difference between revisions
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<math>\hat{\gamma}^{(i)} = y^{(i)}((\frac{w}{\Vert w \Vert})^Tx^{(i)}+\frac{b}{|b|})</math><br> | <math>\hat{\gamma}^{(i)} = y^{(i)}((\frac{w}{\Vert w \Vert})^Tx^{(i)}+\frac{b}{|b|})</math><br> | ||
* <math>\mathbf{w}</math> is the normal vector of our hyperplane so \frac{w}{\Vert w \Vert})^Tx^{(i)} is the length of the projection of x onto our normal vector. | ;Notes | ||
* <math>\mathbf{w}</math> is the normal vector of our hyperplane so <math>\frac{w}{\Vert w \Vert})^Tx^{(i)}</math> is the length of the projection of x onto our normal vector. | |||
: This is the distance to our hyperplane. | : This is the distance to our hyperplane. | ||