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Learning Deep Neural Network Based Kernel Functions for Small Sample Size ClassificationGD) based algorithm is also proposed, which still implements alternating optimization in each iteration. Furthermore, an incremental batch size method is given to reduce gradient noise gradually in optimization process. Experimental results show that our method performed better than the typical meth

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Nuclear Subdomains and Cancer,mponenten. Mitwirkende Faktoren sind darüber hinaus örtliche Verhältnisse, rechtliche Bindungen, einschränkende Vorsehriften, politische Absiehten, Neigungen zu Tradition und Prestige usw. Architekt und Ingenieur haben diese Forderungen aufeinander abzustimmen und damit eine schwere und stets neue A

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