Incremental Learning of Limited Kernel Associative Memory
نویسندگان
چکیده
This paper proposes a limited kernel associative memory, where the number of kernels is limited to a certain number. This model aims to be used on embedded systems with a small amount of storage space. The learning algorithm for the kernel associative memory is an improved version of the limited general regression neural network, which was proposed by one of the authors. In the experiments, we show the LKAM is able to continue incremental learning of new instances by pruning redundant memory. Keywords—limited general regression neural network, limited kernel associative memory, kernel method
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تاریخ انتشار 2011