Y. LeCun. "A learning scheme for asymmetric threshold networks." In: Proceedings of Cognitiva 85, pp.599-604, Paris, France. 1985.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... could be realized by weakening the first assumption and incorporating internal units that adaptively recode the input representation provided by the environment (Rosenblatt, 1962) The subsequent development of algorithms such as Boltzmann learning (Hinton Sejnowski, 1986) and backpropagation (LeCun, 1985; Parker, 1985; Rumelhart, Hinton, Williams, 1986; Werbos, 1974) have provided the means for training networks with adaptive nonlinear internal units. The second assumption has also been weakened learning algorithms that require no explicit teacher have been developed (Becker Hinton, 1989; ....

LeCun, Y. (1985). A learning scheme for asymmetric threshold networks. Proceedings of Cognitiva 85. Paris, France.

....of the early sixties [Bryson and Ho, 1969] but its application to machine learning was not generally realized then. Interestingly, the early derivations of back propagation in the context of neural network learning did not use gradients, but virtual targets for units in intermediate layers [LeCun, 1985, LeCun, 1986] or minimal disturbance arguments [Parker, 1985] The Lagrange formalism used in the control theory literature provides perhaps the best rigorous method for deriving back propagation [LeCun, 1987] and for deriving generalizations of back propagation to recurrent networks [LeCun, ....

LeCun, Y. (1985). A learning scheme for asymmetric threshold networks. In Proceedings of Cognitiva 85, pages 599--604, Paris, France.

....theory literature of the early sixties [16] but its application to machine learning was not generally realized then. Interestingly, the early derivations of back propagation in the context of neural network learning did not use gradients, but virtual targets for units in intermediate layers [17], 18] or minimal disturbance arguments [19] The Lagrange formalism used in the control theory literature provides perhaps the best rigorous method for deriving back propagation [20] and for deriving generalizations of back propagation to recurrent networks [21] and networks of heterogeneous ....

Y. LeCun, "A learning scheme for asymmetric threshold networks, " in Proceedings of Cognitiva 85, Paris, France, 1985, pp. 599--604.

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Y. LeCun. "A learning scheme for asymmetric threshold networks." In: Proceedings of Cognitiva 85, pp.599-604, Paris, France. 1985.

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