Linear classifiers classify data into labels based on a linear combination of input features. Therefore, these classifiers separate data using a line or plane or a hyperplane (a plane in more than 2 dimensions). They can only be used to classify data that is linearly separable. They can be modified to classify non-linearly separable data
A linear mapping: $$ f(x_i, W, b) = W x_i + b $$
In the above equation, we are assuming that the image (x_i) has all of its pixels flattened out to a single column vector of shape [D x 1]. The matrix W (of size [K x D]), and the vector b (of size [K x 1]) are the parameters of the function. In CIFAR-10, (x_i) contains all pixels in the i-th image flattened into a single [3072 x 1] column, W is [10 x 3072] and b is [10 x 1], so 3072 numbers come into the function (the raw pixel values) and 10 numbers come out (the class scores). The parameters in W are often called the weights, and b is called the bias vector because it influences the output scores, but without interacting with the actual data (x_i).