Deep kernel-SVM network

Deep learning techniques have claimed state-of-the-art results in a wide range of tasks, including classification. Despite the promising results, there are limitations for these large networks. In fact, deep neural networks have a poor generalisation performance on small data sets, such as biologic data. This paper describes a new machine learning algorithm for classification tasks. We introduce a Multi-Layer Multiple Kernel Learning (ML-MKL) framework. The input data are first transformed through a set of weighted non-linear kernel functions in a multilayer structure. Then, an SVM classifier is used to make the final decision. The proposed network is trained to minimize the error function. Indeed, we propose to optimize the network over an adaptive backpropagation algorithm. The generalization performance of the proposed method is compared over various state-of-the-art multiple kernel algorithms on several benchmark and two real world applications, including object recognition and spoken language recognition. Experimental results show that the ML-MKL generally outperforms existing kernel methods.