Abstract:

Online sequential kernel extreme learning machine (OSELM) is an increment classification algorithm, and it only keeps training model at last time, then adjusts the original model from the current samples. However, it does not batch calculation when solving the problem of realtime dynamic data classification. This method by minimizing the empirical risk leads to the overfitting, and randomly assigns hidden layer neurons in offline training, which makes the model have poor robust. Moreover, the solving process is difficult to be extended to the kernel method, which reduces the classification accuracy. Pointing to abovementioned problems, a new online classification method, selfcompounding kernels OSELM （SCKOSELM）, is proposed based on the kernel method. Firstly, inputted samples are mapped to multikernel spaces to obtain different features, and the nonlinear combination of features are calculated. Proposed selfcompounding kernels method is used to others supervised kernel methods. Secondly, the prior distribution of training samples as model weights are introduced to maintain the model generalization, and by using the super weight to make the posterior distribution of weights to zero, thus sparse parameter is achieved. Finally, the parameter of sparse are incorporated into the next moment common operations. Numerical experiments indicate that the proposed method is effective．In comparison with OSELM, the proposed method has better performance in the sense of stability and classification accuracy, and is suitable for realtime dynamic data classification.