simple classifier transformation that converts feature vectors into recognition scores
for Computer Vision
- input (x): image features (pixels, Histogram of Oriented Gradients (HoG), etc.)
- weights (w): learned params
- output (y): scores for each class
interpretation
- each row of W is a template for a class
- High inner product (similarity) → high confidence
see linear classifier linear models are the foundation for neural networks