[外文翻译]反向传播/backpropagation.rar
[外文翻译]反向传播/backpropagation,[外文翻译]反向传播/backpropagation内包含中文翻译英文原文，内容齐全，建议下载阅览。①中文页数 23中文字数 9903②英文页数 31英文字数 36000③ 摘要 frank rosenblat:的感知机学习规则和bernard widrow和marcian hnff的lms算法是设计用来训练单层的类似...
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[外文翻译]反向传播/BACKPROPAGATION
内包含中文翻译英文原文，内容齐全，建议下载阅览。
①中文页数 23
中文字数 9903
②英文页数 31
英文字数 36000
③ 摘要
Frank Rosenblat:的感知机学习规则和Bernard Widrow和Marcian Hnff的LMS算法是设计用来训练单层的类似感知器的网络的。这些单层网络的缺点是只能解线性可分的分类间题。Rosenblat，和Pvidrow均意识到这些限制并且都提出了克服此类问题的方法:多层网络。但他们未将这类算法推广到用来训练功能更强的网络。
Paul Werboss在他1974年的论文中第一次描述了训练多层神经网络的一个算法，论文中的算法是在一般网络的情况中描述的，而将神经网络作为一个特例。论文没有在神经网络研究圈子内传播。直到20世纪80年代中期，反向传播算法才重新被发现并广泛地宣扬，它是被David Ftumelhart, Geoffrey Hinton和Ronald Williaras , David Parkerr ,以及Yann Le Cun分别独立地重新发现的。这个算法因被包括在《并行分布式处理》( Parallel Distributed Processing)一书中而得到普及。这本书介绍心理学家David Rumelhart和James McClelland领导的并行分布处理小组所做的研究工作。这本书的出版引发了神经网络的研究热潮。当前，用反向传播算法训练的多层感知机是应用最广的神经网络
The perceptron learning rule of Frank Rosenblatt and the LMS algorithm of Bernard Widrow arid Martian Hotf were designed to train single-layer perceptron-like networks. these single-layer networks suffer from the disadvantage that they are only able to solve linearly separable classification pmhlems. Both Rosenblatt and Widraw were aware of these limitations and purposed multilayer networks that could overcome them, but they were not able to generalize their also-rithms to train these more powerful networks.
Apparently the first description of an algorithm to train multilayer networks was contained in the thesis of Paul Werhos in 1974. This thesis presented the algorithm in the context of general networks, with neural networks as a special case, and was not disseminated in the neural
④ 关键字 多层感知机/Muitilayer Per
内包含中文翻译英文原文，内容齐全，建议下载阅览。
①中文页数 23
中文字数 9903
②英文页数 31
英文字数 36000
③ 摘要
Frank Rosenblat:的感知机学习规则和Bernard Widrow和Marcian Hnff的LMS算法是设计用来训练单层的类似感知器的网络的。这些单层网络的缺点是只能解线性可分的分类间题。Rosenblat，和Pvidrow均意识到这些限制并且都提出了克服此类问题的方法:多层网络。但他们未将这类算法推广到用来训练功能更强的网络。
Paul Werboss在他1974年的论文中第一次描述了训练多层神经网络的一个算法，论文中的算法是在一般网络的情况中描述的，而将神经网络作为一个特例。论文没有在神经网络研究圈子内传播。直到20世纪80年代中期，反向传播算法才重新被发现并广泛地宣扬，它是被David Ftumelhart, Geoffrey Hinton和Ronald Williaras , David Parkerr ,以及Yann Le Cun分别独立地重新发现的。这个算法因被包括在《并行分布式处理》( Parallel Distributed Processing)一书中而得到普及。这本书介绍心理学家David Rumelhart和James McClelland领导的并行分布处理小组所做的研究工作。这本书的出版引发了神经网络的研究热潮。当前，用反向传播算法训练的多层感知机是应用最广的神经网络
The perceptron learning rule of Frank Rosenblatt and the LMS algorithm of Bernard Widrow arid Martian Hotf were designed to train single-layer perceptron-like networks. these single-layer networks suffer from the disadvantage that they are only able to solve linearly separable classification pmhlems. Both Rosenblatt and Widraw were aware of these limitations and purposed multilayer networks that could overcome them, but they were not able to generalize their also-rithms to train these more powerful networks.
Apparently the first description of an algorithm to train multilayer networks was contained in the thesis of Paul Werhos in 1974. This thesis presented the algorithm in the context of general networks, with neural networks as a special case, and was not disseminated in the neural
④ 关键字 多层感知机/Muitilayer Per
