搜索资源列表
PerceptionApproach
- 模式识别的经典算法之一,感知器算法,用来对模式进行分类,采用matlab编写。-the classic pattern recognition algorithm, perception algorithm, the model used for classification, prepared using Matlab.
chapter5_45
- 这是模式分类一书中第五章的固定增量感知器和带裕量的变增量感知器的matlab代码实现。-This is the pattern classification of a book chapter V and the fixed-increment perceptron with margin perceptron incremental change of the matlab code.
fenlei
- 感知器分类,使用matlab-Single-layer perceptron classification
pattern_recognition
- 感知器MATLAB的算法,能够很好的实现感知器的分类-gan zhi qi matlab suanfa shixian
Perceptron
- matlab语言编写的iris数据的单层感知器分类-matlab data written in a single layer perceptron iris classification
four
- 基于matlab语言,利用BP神经网络设计一个感知器,对输入进行分类-Based on the matlab language, the use of BP neural network to design a sensor, the input classification. .
ganzhiqi
- 神经网络感知器模型程序,可以对现行可分系统进行分类。用matlab实现。另附BP算法文档。-Neural network sensor model program that can be divided into the current classification system. Using matlab. BP algorithm attached document.
ganzhiqi_g7
- 基于感知器的iris数据集分类算法,matlab实现,有注释。-Classification algorithm based on the iris data set of the perceptron, the Matlab implementation, and comment.
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
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
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
perceptron
- 用matlab库实现的感知器对二维向量进行分类,内包含数据-Implementation of perceptron to classif two-dimensional vectors using Matlab library. With data inside.
Linear-learner
- 基于PCA的线性学习器的分类方法,含完整Matlab程序及训练测试集,用于人脸识别。-Linear learner
EXP2
- 利用Matlab神经网络工具箱中感知器函数对二维线性可分的样本进行正确分类。-Use Matlab neural network toolbox Perceptron dimensional linearly separable function correctly classified samples.
9、SVM方法
- svm分类器,训练svm的MATLAB代码,简单易理解,好用,能够有效的实现动能(SVM classifier, training SVM MATLAB code, simple and easy to understand, good use, can effectively implement the kinetic energy)
89123030face_recognition_adaBoost_M2
- 有级联分类器和haar特征和积分图,内部有matlab程序,适合初学者使用(There are cascading classifiers and Haar features and integral graphs, with MATLAB programs inside, suitable for beginners to use)
code
- matlab单层神经网络实现与逻辑,感知器是一种最简单的神经网络,可以解决最简单分类问题。在本经验中,利用了MATLAB代码简单实现了一个单层神经网络的感知器,对“与”逻辑运算进行了训练和学习,以便我们深入地了解感知器的构造。(Implementation and logic of MATLAB single layer neural network)
模式识别代码
- 基于matlab的Iris、乳腺癌数据集的模式识别分类算法,含有 遗传算法+SVM、isodata、感知器算法、LMSE、神经网络等算法的实现代码,用于聚类效果良好,是模式识别大作业的参考资料(The pattern recognition classification algorithm based on MATLAB for Iris and breast cancer data sets contains the implementation code of genetic algorit
贝叶斯判决
- 假定某个局部区域细胞识别中正常w1和非正常w2 两类先验概率分别为: 正常状态:P(w1)=0.9 ; 异常状态:P(w2)=0.1 。 现有一系列待观察的细胞,其观察值为: -2.67 -3.55 -1.24 -0.98 -0.79 -2.85 -2.76 -3.73 -3.54 -2.27 -3.45 -3.08 -1.58 -1.49 -0.74 -0.42 -1.12 4.25 -3.99 2.88 -0.98 0.79 1.19 3.07 两类的类条件概率符合正态分布