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感知准则函数
- 感知准则函数,包括固定增量法和梯度下降法,都是模式识别中的基础算法.-perceptual function criteria, including fixed increment and the gradient method, which is pattern recognition algorithm based.
ganzhiqi_s200502106
- 模式识别-基于感知函数准则的线性分类器设计，完全自编代码，有详细说明。-pattern recognition-based perceptual function criteria for the classification of linear design, completely writing code is described in detail.
87564grace
- 用matlab完成感知准则函数确定程序的设计，写出准则函数，详细说明实验原理。-using Matlab perceptual function for the completion of the procedures for determining the design, write guidelines function, detailed experimental principle.
感知准则函数
- 感知准则函数,包括固定增量法和梯度下降法,都是模式识别中的基础算法.-perceptual function criteria, including fixed increment and the gradient method, which is pattern recognition algorithm based.
ganzhiqi_s200502106
- 模式识别-基于感知函数准则的线性分类器设计，完全自编代码，有详细说明。-pattern recognition-based perceptual function criteria for the classification of linear design, completely writing code is described in detail.
87564grace
- 用matlab完成感知准则函数确定程序的设计，写出准则函数，详细说明实验原理。-using Matlab perceptual function for the completion of the procedures for determining the design, write guidelines function, detailed experimental principle.
PRtwo
- 感知准则函数，可以把前三组数据很好的分离出来-Perceptron criterion algorithm experiment
Untitled2
- 用感知准则函数的方法求解以下数据的判决面，学习率为 ，画出每次迭代法向量的变化轨迹，并画出最终的判决曲线。 -The Perceptron criterion function method for solving the decision following data side, learning rate, draw vector for each iteration the trajectory, and draw the c
perceptron
- 实现批量样本修*感知准则函数的程序。MATLAB程序，课后作业-PERCEPTION ALGORITHM
ganzhi
- 感知准则函数是五十年代由Rosenblatt提出的一种自学习判别函数生成方法，Rosenblatt企图将其用于脑模型感知器，因此被称为感知准则函数。其特点是随意确定的判别函数初始值，在对样本分类训练过程中逐步修正直至最终确定。 -Perception criterion function is the 1950s proposed by Rosenblatt, a self-learning discriminant functio
Perceptual
- 感知准则梯降法求线性判别函数权向量的算法-using perceptual criterion to find weightvector of a linear decision function
Perceptron
- 本实验的目的是学习和掌握两种感知器算法：批处理感知器算法和批处理裕量松弛 算法。感知器算法是通过学习两类已标记的样本，建立一个线性分类器。学习的过程就是求解感知器权系数的过程，人们通过建立一个准则函数J(a),将求解感知器权系数的问题简化为一个标量函数J(a)的极小化问题，即当a为解向量时，J(a)最小。而极小化问题常用梯度下降法来解决。本实验给出了基于梯度下降法的两种感知器算法，介绍了原理并编程实现，最后对两种算法的特点加以比较分