感知机

We assume that each image (grayscale) is represented as a column vector x of dimension d. So, the pixel intensity values in the image, column by column, are concatenated into a single column vector. If the image has 100 by 100 pixels, then d = 10000. We assume that all the images are of the same size. Our classifier is a binary valued function f : Rd → {−1, 1} chosen on the basis of the training set alone.

 

Now that we have chosen a function class (perhaps suboptimally) we still have to find a specific function in this class that works well on the training set. This is often referred to as the estimation problem