1.1 --- a/grant.html Tue Apr 21 17:35:00 2009 -0700 1.2 +++ b/grant.html Tue Apr 21 18:53:40 2009 -0700 1.3 @@ -664,9 +664,9 @@ 1.4 We will explore and compare different classifiers. As noted above, this activity is not separate from the previous one, 1.5 because some supervised learning algorithms include feature selection, and any classifier can be combined with a stepwise 1.6 wrapper for use as a feature selection method. We will explore logistic regression (including spatial models[15]), decision 1.7 -trees20 , sparse SVMs, generative mixture models (including naive bayes), kernel density estimation, genetic algorithms, and 1.8 -artificial neural networks. 1.9 -Decision trees 1.10 +trees20 , sparse SVMs, generative mixture models (including naive bayes), kernel density estimation, instance-based learning 1.11 +methods (such as k-nearest neighbor), genetic algorithms, and artificial neural networks. 1.12 +Application to cortical areas 1.13 # confirm with EMAGE, GeneAtlas, GENSAT, etc, to fight overfitting, two hemis 1.14 Develop algorithms to suggest a division of a structure into anatomical parts 1.15 1.Explore dimensionality reduction algorithms applied to pixels: including TODO