1.1 --- a/grant.txt Mon Apr 13 03:25:42 2009 -0700 1.2 +++ b/grant.txt Mon Apr 13 03:31:42 2009 -0700 1.3 @@ -29,7 +29,7 @@ 1.4 1.5 The object of aim 1 is not to produce a single classifier, but rather to develop an automated method for determining a classifier for any known anatomical structure. Therefore, we seek a procedure by which a gene expression dataset may be analyzed in concert with an anatomical atlas in order to produce a classifier. Such a procedure is a type of a machine learning procedure. The construction of the classifier is called __training__ (also __learning__), and the initial gene expression dataset used in the construction of the classifier is called __training data__. 1.6 1.7 -In the machine learning literature, this sort of procedure may be thought of as a __supervised learning task__, defined as a task in whcih the goal is to learn a mapping from instances to labels, and the training data consists of a set of instances (voxels) for which the labels (subregions) are known. 1.8 +In the machine learning literature, this sort of procedure may be thought of as a __supervised learning task__, defined as a task in which the goal is to learn a mapping from instances to labels, and the training data consists of a set of instances (voxels) for which the labels (subregions) are known. 1.9 1.10 Each gene expression level is called a __feature__, and the selection of which genes to include is called __feature selection__. Feature selection is one component of the task of learning a classifier. Some methods for learning classifiers start out with a separate feature selection phase, whereas other methods combine feature selection with other aspects of training. 1.11 1.12 @@ -316,3 +316,5 @@ 1.13 --- 1.14 1.15 note: 1.16 + 1.17 +do we need to cite: no known markers? impressive results?