“SVMs are wonderful as a generic classification method with beautiful math behind them. But in the end, they are nothing more than simple two-layer systems. The first layer can be seen as a set of units (one per support vector) that measure a kind of similarity between the input vector and each support vector using the kernel function. The second layer linearly combines these similarities.”

“It’s a two-layer system in which the first layer is trained with the simplest of all unsupervised learning method: simply store the training samples as prototypes in the units. Basically, varying the smoothness of the kernel function allows us to interpolate between two simple methods: linear classification, and template matching. I got in trouble about 10 years ago by saying that kernel methods were a form of glorified template matching.”

“Performing image recognition with invariance to shifts, scale, rotation, lighting conditions, and background clutter was impossible (or extremely inefficient) for a kernel machine operating at the pixel level. But it was quite easy for deep architectures such as convolutional nets.” Yann LeCun