Support vector machine + Evolutionary Algorithm = ?
Evolutionary Computation, Support Vector Machines October 17th. 2007, 9:22pmCao, L., Francis, E. (2003) Support Vector Machines with Adaptive Parameters in Financial Timeseries forecasting, IEEE Transactions on Neural Networks, 14(6):1506:1518
Now here is a point of thought: Support vector machines + Evolutionary Algorithms = ?
On the surface, it appears that support vector machines beat neural networks in terms of maintaining generality on both in-sample and out of sample data when applied to forecasting. But the problem is deciding what kernel function to use for a particular forecasting task using support vectors? This problem in some ways is analogous to the choice of connectionist structure to use for a neural network. Perhaps some kind of evolutionary algorithm can be applied to determine the best kernel function, given a population of kernel functions. This might be an idea for an upcoming paper.
