CNN Approaches for Time Series Classification DOI: http://dx.doi.org/10.5772/intechopen.81170

In that sense, we propose a "Transfer learning with SVM read-out" framework which is composed of two parts: (i) the first part having first and intermediate layers' weights of a CNN already pre-trained on a source learning task, (the last CNN layer being discarded), and (ii) the second part composed of a support vector machine (SVM) classifier with RBF kernel which is connected to the end of the first part. Then, we feed the entire training dataset of the target task into this framework in order to train the SVM parameters. As opposed to training a CNN on the target task which requires updating all hidden layers' weights for several iterations using a large training set for all these weights to converge, our framework computes weights of the last layer(s) only, in one iteration only. Moreover the advantage of using SVM as the classifier is that it is fast and generally performs well on small training set since it only relies on the support vectors, which are the training samples that lay exactly on the hyperplanes used to define the margin. In addition, SVMs have the powerful RBF kernel, which allows to map the data to a very high dimension space in which the data can be separable by a hyperplane, hence guaranteeing convergence. Hence, our framework can be regarded as a global, fast and light-weight technique for time series classification where the target task has limited annotated/labeled data.
