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* [[Naive Bayes_OldKiwi]]-- What it is and why everyone should know about it. | * [[Naive Bayes_OldKiwi]]-- What it is and why everyone should know about it. | ||
* [[Philosophies of Machine Learning_OldKiwi]] -- A long article | * [[Philosophies of Machine Learning_OldKiwi]] -- A long article | ||
| − | * | + | * Lower bound on performance of [[Bayes Classification_OldKiwi]] is <math>\frac{1}{2}</math> when the number of classes is 2 |
| − | * | + | * Ideal performance of [[Bayes Classification_OldKiwi]] 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_OldKiwi]] as a function of dimensions, covariance, etc. | * [[Amount of training data needed_OldKiwi]] as a function of dimensions, covariance, etc. | ||
* [[Classification of data not in the Reals_OldKiwi]] (<math>\mathbb{R}^n</math>), such as text documents and graphs | * [[Classification of data not in the Reals_OldKiwi]] (<math>\mathbb{R}^n</math>), such as text documents and graphs | ||
Latest revision as of 08:49, 17 April 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My webpage is little out of date, but you can visit it anyway!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!
- Testing, Training, and Cross-Validation Data_OldKiwi -- Everyone should know what each of these are!
- Using LibSVM effectively_OldKiwi -- a brief review of what they already show in their documentation.
- Naive Bayes_OldKiwi-- What it is and why everyone should know about it.
- Philosophies of Machine Learning_OldKiwi -- A long article
- Lower bound on performance of Bayes Classification_OldKiwi is $ \frac{1}{2} $ when the number of classes is 2
- Ideal performance of Bayes Classification_OldKiwi 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_OldKiwi as a function of dimensions, covariance, etc.
- Classification of data not in the Reals_OldKiwi ($ \mathbb{R}^n $), such as text documents and graphs
- Fisher's Linear Discriminant_OldKiwi -- 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_OldKiwi like someone has done manually at the bottom of every page.
