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• W2022173618 startingPage "459" @default.
• W2022173618 abstract "Various algorithms for constructing weight matrices for Hopfield-type associative memories are reviewed, including ones with much higher capacity than the basic model. These alternative algorithms either iteratively approximate the projection weight matrix or use simple perceptron learning. An experimental investigation of the performance of networks trained by these algorithms is presented, including measurements of capacity, training time and their ability to correct corrupted versions of the training patterns." @default.
• W2022173618 created "2016-06-24" @default.
• W2022173618 creator A5017981647 @default.
• W2022173618 creator A5050002935 @default.
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• W2022173618 date "2004-12-01" @default.
• W2022173618 modified "2023-09-27" @default.
• W2022173618 title "High capacity recurrent associative memories" @default.
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• W2022173618 doi "https://doi.org/10.1016/j.neucom.2004.02.007" @default.
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