Support vector machines for olfactory signals recognition

Pattern recognition techniques have widely been used in the context of odor recognition. The recognition of mixtures and simple odors as separate clusters is an untractable problem with some of the classical supervised methods. Recently, a new paradigm has been introduced in which the detection problem can be seen as a learning from examples problem. In this paper, we investigate odor recognition in this new perspective and in particular by using a novel learning scheme known as support vector machines (SVM) which guarantees high generalization ability on the test set. We illustrate the basics of the theory of SVM and show its performance in comparison with radial basis network and the error backpropagation training method. The leave-one-out procedure has been used for all classifiers, in order to finding the near-optimal SVM parameter and both to reduce the generalization error and to avoid outliers.