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gradient descent中文是什么意思

  • 梯度下降

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  • 例句与用法
  • Is based on the idea of gradient descent
    规则是基于梯度降落这样一种思路。
  • Based on gradient descent rule , the bp ( back propagation ) algorithm is a local optimization algorithm
    Bp算法基于梯度下降原理,是一种局部寻优算法。
  • A comparative study on some typical improved models of bp networks , which based on gradient descent and numerical optimization are proposed
    全局优化改进策略从基于网络模型的优化和基于网络算法的优化两个方面考虑。
  • The weights are trained with gradient descent method . the increase algorithm of bvs , and restricted algorithm , was induced
    利用梯度下降法对网络的权值进行训练,并且推导了bvs的增长算法,以及网络训练的限制记忆递推公式。
  • The essence of back propagation networks is that make the change of weights become little by gradient descent method and finally attain the minimal error
    其实质是采用梯度下降法使权值的改变总是朝着误差变小的方向改进,最终达到最小误差。
  • The parameter of local model can be calculated by gradient descent in neighborhood with the sofm weight together , or estimated by least - squared estimation ( lse )
    局部模型的参数既可和映射网络权值一起在邻域内采用梯度下降法修正,也可结合最小二乘法得到其最佳估计。
  • Video technol . , aug . 1994 , 4 : 438 - 442 . 4 liu l k , peig e . a block - based gradient descent search algorithm for block motion estimation in video coding
    这些算法适合于用在运动较为平缓的情况下,但是在运动较为剧烈的情况下,这些算法很有可能陷入局部最优,因而造成搜索精度损失。
  • The back - propagation algorithm also rests on the idea of gradient descent , and so the only change in the analysis of weight modification concerns the difference between t and y
    反向传播算法同样来源于梯度降落原理,在权系数调整分析中的唯一不同是涉及到t ( p , n )与y ( p , n )的差分。
  • A gradient descent learning algorithm is employed to train the network . finally , the whole thesis is summarized , and some future research areas are highlighted
    最后,对全文作出总结,归纳了本文解决的问题,指出了基于多元统计投影软测量技术的今后值得关注和深入研究的方向。
  • In this scheme , the inputs of hidden layer neurons are acquired by using the gradient descent method , and the weights and threshold of each neuron are trained using the linear least square method
    在该方案中,通过梯度法获取隐层神经元的输入,使用线性最小二乘法训练各神经元的权值和阈值。
  • 更多例句:  1  2  3
  • 百科解释
Gradient descent is a first-order optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point.
详细百科解释
  • 推荐英语阅读
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