Deep Multiple Kernel Learning

Deep learning methods construct new features by transforming the input data through multiple layers of nonlinear processing. This has conventionally been accomplished by training a large artificial neural network with several hidden layers. However, the method has been limited to datasets with very large sample sizes such as the MNIST dataset which contains 60,000 training samples. More recently, there has been a drive to apply deep learning to datasets with more limited sample sizes as typical in many real-world situations. Kernel methods have been particularly successful on a variety of sample sizes because they can enable a classifier to learn a complex decision boundary with only a few parameters by projecting the data onto a high-dimensional reproducing kernel Hilbert space.