An Idiot’s guide to Support vector machines (2003) [pdf]
Back in around 2008, SVMs were all the rage in computer vision. We would use hand designed visual features and then a linear SVM on top. That was how object detectors were built (remember DPM?)<p>Funny how SVMs are just max-margin loss functions and we just took for granted that you needed domain expertise to craft features like HOG/SIFT by hand.<p>By 2018, we use ConvNets to learn BOTH the features and the classifier. In fact, it’s hard to separate where the features end and the classifier begins (in a modern CNN).