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RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates. TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. TFA was put forward to overcome this problem ,but it is not perfect...
In this paper we discuss the limit cycle property of a family of Brain-state-in-a-Box (BSB) models with delay. The dynamics of the model is addressed using analysis method. For the network with arbitrary weight matrix, the conditions for the existence of cycle of length 2 are presented. The advantage of the method is the ability to be predicted limit cycles in updating initial three steps. At last,...
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