Description: 包含用lms、mse、perceptron准则函数的二类分类器-Includes use of lms, mse, perceptron criterion function of the second-class classifier Platform: |
Size: 1875968 |
Author:黎维娟 |
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Description: 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 curve of the final decision. Platform: |
Size: 1024 |
Author:liqiang |
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Description: 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析。-The purpose of this study is to learn and master the two kinds of perceptron algorithm: batch sensor algorithm and batch processing margin slack
Algorithm. Perceptron algorithm is by learning two kinds of labeled samples, the establishment of a linear classifier. The process of learning is the process of solving the perceptron weights, people through the establishment of a criterion function J (a), the weighting coefficients for solving perceptron problem is reduced to a minimization problem for a scalar function J (a), i.e. when a solution vector, J (a) minimum. The minimization problem commonly used gradient descent method to solve. This paper gives two kinds of perceptron algorithm based on gradient descent method, introduces the principle and program implementation, and finally compares the characteristics of the two algorithms. Platform: |
Size: 1024 |
Author:Jam_Jack |
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Description: HK算法思想很朴实,就是在最小均方误差准则下求得权矢量.
他相对于感知器算法的优点在于,他适用于线性可分和非线性可分得情况,对于线性可分的情况,给出最优权矢量,对于非线性可分得情况,能够判别出来,以退出迭代过程.(The idea of HK algorithm is very simple, which is to obtain the weight vector under the minimum mean square error criterion.
Compared with the perceptron algorithm, it is suitable for linear separable and non-linear separable cases. For linear separable cases, the optimal weight vector is given. For non-linear separable cases, it can be distinguished to exit the iteration process.) Platform: |
Size: 17813504 |
Author:mao12345 |
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