From KNN to Nearest Neighbor Classification
A slecture by ECE student Jonathan Manring
Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
Post your slecture material here. Guidelines:
- If you are making a text slecture
- Type text using wikitext markup languages
- Type all equations using latex code between <math> </math> tags.
- You may include links to other Project Rhea pages.
Euclidean distance:
$ D(\vec{x_1},\vec{x_2})=||\vec{x_1}-\vec{x_2}||_{L_2}=\sqrt{\sum_{i=1}^n ({x_1}^i-{x_2}^i)^2} $
Manhattan (cab driver) distance:
$ D(\vec{x_1},\vec{x_2})=||\vec{x_1}-\vec{x_2}||_{L_1}=\sum_{i=1}^n |{x_1}^i-{x_2}^i| $
Minkowski metric:
$ D(\vec{x_1},\vec{x_2})=||\vec{x_1}-\vec{x_2}||_{L_p}=(\sum_{i=1}^n ({x_1}^i-{x_2}^i)^p)^{\frac{1}{p}} $
Riemannian metric:
$ D(\vec{x_1},\vec{x_2})=\sqrt{(\vec{x_1}-\vec{x_2})^\top \mathbb{M}(\vec{x_1}-\vec{x_2})} $
Infinite norm:
$ D(\vec{x_1},\vec{x_2})=||\vec{x_1}-\vec{x_2}||_{\infty}=max_i |{x_1}^i-{x_2}^i| $
(create a question page and put a link below)
Questions and comments
If you have any questions, comments, etc. please post them on this page.
