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* [[Fisher's Linear Discriminant_Old Kiwi]] -- Why it is ideal in the case of equal-variance Gaussians, a derivation that is less heuristic than the traditional development. | * [[Fisher's Linear Discriminant_Old Kiwi]] -- Why it is ideal in the case of equal-variance Gaussians, a derivation that is less heuristic than the traditional development. | ||
| − | + | Administrative stuff to do: | |
| + | * Copying stuff over from the old kiwi! | ||
| + | * Create a Lecture [[Template_Old Kiwi]] like someone has done manually at the bottom of every page. | ||
Revision as of 16:24, 25 March 2008
Hi! I'm Josiah Yoder, and I'm a big fan of Kiwis... and wikis.
My webpage is little out of date, but you can visit it anyway!
TODO
There are several articles I would like to write on the Kiwi when I get the time. If you would like to write them instead, please go for it, and let me know!
- Lower bound on performance of Bayes Classification_Old Kiwi is $ \frac{1}{2} $ when the number of classes is 1
- Ideal performance of Bayes Classification_Old Kiwi when the two classes are Gaussian with the same variance and prior probability can be computed exactly, even when there is correlation between the dimensions
- Amount of training data needed_Old Kiwi as a function of dimensions, covariance, etc.
- Naive Bayes_Old Kiwi-- What it is and why everyone should know about it.
- Classification of data not in the Reals_Old Kiwi ($ \mathbb{R}^n $), such as text documents and graphs
- Fisher's Linear Discriminant_Old Kiwi -- Why it is ideal in the case of equal-variance Gaussians, a derivation that is less heuristic than the traditional development.
Administrative stuff to do:
- Copying stuff over from the old kiwi!
- Create a Lecture Template_Old Kiwi like someone has done manually at the bottom of every page.
