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The notion of kernels, recently introduced, has drawn much interest as it allows one to obtain nonlinear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the support vector machines (SVMs), has produced significant progress in machine learning and related research topics. The success of such algorithms is now spreading as they are applied to more and more domains. Signal processing procedures can benefit from a kernel perspective, making them more powerful and applicable to nonlinear processing in a simpler and nicer way. We present an overview of kernel methods and provide some guidelines for future development in kernel methods, as well as, some perspectives to the actual signal processing problems in which kernel methods are being applied.