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FIG002
- Rosenblatt感知机Matlab实现算法线性分类器的第一个迭代算法是1956年由Frank Rosenblatt提出的. 这个算法被提出后, 受到了很大的关注. 感知器在神经网络发展的历史上占据着特殊的位置: 它是第一个从算法上完整描述的神经网络. 在20世纪60年代和70年代, 受感知器的启发, 工程师, 物理学家以及数学家们纷纷投身于神经网络不同方面的研究. -Rosenblatt perceptron Matlab algorithm linear classifier, an
K-means-and-Perceptron
- 该程序为matlab程序,共有三个文件,dataC.m为程序入口,实现功能对50组数据用k均值算法进行分类,再对40组数据用感知器算法训练,然后用训练得到的判别函数对剩下10组数据分类,最后与原始分类做差比较,若分类无误,则全显示为0.-Matlab program on the program, a total of three files dataC.m for program entry features 50 sets of data with k-means algorithm to
target-context-detect-recognition
- 在分类器的研究方面,提出了一种改进的AdaBoost算法,包括距离相关的判别准则,和基于核的感知器强分类器优化方法 -Classifier, there is proposed an improved AdaBoost algorithm, including the distance related criterion optimization method, and the strong classifier is based on the perception of the nucle
Improved-ICA-character-recognition
- 该算法一种结合改进的基于独立分量分析(ICA)提取算法和基于多层感知器和单向二叉决策树的多类支持向量机分类方法。-The algorithm is a combination of improved based on independent component analysis (ICA) algorithm and multi-class support vector machine classification method based on binary decision tree of
perceptron-Cpp
- 单层感知器算法,用于实现线性分类的重要算法-Single-layer perceptron algorithm
perceptron
- 基于VC++实现感知器模式识别,并用OPENGL显示分类结果-The perceptron Pattern Recognition based VC++ OPENGL classification results
Perceptron-Approach-with-matlab-
- 用matlab软件编写感知器算法,实现对样本的分类,样本点为X1(0,0),X2(0,-1),X3(-1,0),X4(-1,-1) -Perceptron Approach with matlab
artifical-nerual-network
- 人工神经网络感知器,利用样本点训练网络并绘出得到的分类线-artifical nerual network
classifer
- 二分类问题采用包括逻辑回归、最小二乘法、感知器算法(按下space不断迭代)、svm线性分类,另外还有高斯分线性分类(待完善),针对平面上两类点进行分类-Second classification using logistic regression, the method of least squares, perception algorithm (Press space iteratively) svm linear classification, in addition to the Ga
Perceptron-Algorithm
- 模式识别是对样本进行聚类,感知器算法是通过迭代计算修正权向量,使样本满足条件,从而实现分类。本程序对感知器算法进行了改进,当权向量不满足时,立即退出此轮计算,进入下一轮迭代,从而减少了计算次数。程序对代码有详细注释,对样本的个数和维数自动判别。-Pattern recognition, clustering samples, perception algorithm is iterative correction weight vector samples meet the conditions
gzq
- 感知器算法,可以实现线性分类,中间步骤很详细,明确表示算法过程-Perception algorithm can achieve linear classification, an intermediate step in great detail, expressly algorithm process
perception_two
- 对两类的问题采用感知器算法进行分类,该方法能够有效的计算出代价函数-Two types of problems with the perception algorithm for classification, this method can effectively calculate the cost function.
pattern-recognizer
- 用matlab软件编写感知器算法,实现对样本的分类,样本点为X1(0,0),X2(0,-1),X3(-1,0),X4(-1,-1) X1,X2属于第一类,X3、X4属于第二类;(编程) X1、X4属于第一类,X2,X3属于第二类;(计算)-Perceptron Algorithm matlab software, to achieve the classification of samples, sample points X1 (0,0), X2 (0,-1), X3 (-1,0
perceptron
- 模式识别-梯度下降法特例的感知器算法的Matlab实现,实现两类线性分类。-Pattern-Recognition,The perceptron algorithm based on MATLAB
1
- 双输入单输出系统 x1(1)=1 x2(1)=1 d(1)=1 x1(2)=-0.5 x2(1)=-1 d(1)=-1 x1(3)=3 x2(1)=1 d(1)=1 x1(4)=-2 x2(1)=-1 d(1)=-1 建立一个感知器网络,实现上述样本的分类,计算出相应的网络权值矩阵W-Dual-input single-output system x1 (1) = 1 x2 (1) = 1 d (1) = 1 x1 (2) =-0.5 x2 (1) =-1 d (1) =-
Neural-network-classified
- 神经网络系统,关于感知器程序,对如下输入、输出样本进行分类,输入样本如下: 所对应的输出的10组二元目标矢量为: -Neural network system perceptron procedures classified as input and output samples and input samples as follows: corresponding to the output of the 10 groups of the binary target vector:
Linear-classification
- 基于感知器算法的线性分类实例,对鸢尾花Iris数据进行两两分类-Linear classification based on perceptron algorithm to the data of Iris
perception_
- 是用神经网络中的感知器进行分类的程序,并给出了图形显示的结果-A program to classify with the perceptron in the NN,and the result is given in the form of chart.
moshishibie
- 对X应用贝叶斯分类,对X的前两类训练单层感知器-The classification of X application of Bias,on the X of two kinds of training single-layer perceptron
final2cop
- matlab用bp神经网络分类信号,采用多层感知器的神经网络,有隐含层5个节点-matlab bp neural network classification signal, the use of Multilayer Perceptron neural network hidden layer nodes