Iterative learning cascaded multiclass kernel based support vector machine for neural spike data classification

In this paper, we develop an iterative learning framework based on multiclass kernel support vector machine (SVM) for adaptive classification of neural spikes. For efficient algorithm execution, we transform a multiclass problem with the Kesler´s construction and extend iterative greedy optimization reduced set vectors approach with a cascaded method. Since obtained classification function is highly parallelizable, the problem is sub-divided and parallel units are instantiated for the processing of each sub-problem via energy-scalable kernels. After partition of the data into disjoint subsets, we optimize the data separately with multiple SVMs. We construct cascades of such (partial) approximations and use them to obtain the modified objective function, which offers high accuracy, has small kernel matrices and low computational complexity.