搜索资源列表
Classify_Homework
- 模式识别作业——用平均样本法，平均距离法，最近邻法和K近邻法进行分类-pattern recognition operations -- with the average sample, the average distance, nearest neighbor and K-nearest-neighbor classification
最近邻法分类器演示
- 本程序是一个最近邻分类算法的演示程序，本程序完成了三种最近邻的演示并实现算法的分析-this procedure is a nearest neighbor classification algorithm the demo program, the completion of a three- Nearest Neighbor algorithm demonstration and analysis
Classify_Homework
- 模式识别作业——用平均样本法，平均距离法，最近邻法和K近邻法进行分类-pattern recognition operations-- with the average sample, the average distance, nearest neighbor and K-nearest-neighbor classification
knn
- 朴素贝叶斯(Naive Bayes, NB)算法是机器学习领域中常用的一种基于概率的分类算法，非常简单有效。k近邻法(k-Nearest Neighbor, kNN)[30,31]又称为基于实例(Example-based, Instance-bases)的算法，其基本思想相当直观：Rocchio法来源于信息检索系统，后来最早由Hull在1994年应用于分类[74]，从那以后，Rocchio方法就在文本分类中广泛应用起来。-Naive
PatternRecognition
- 1.Fisher分类算法 2.感知器算法 3.最小二乘算法 4.快速近邻算法 5.K-近邻法 6.剪辑近邻法和压缩近邻法 7.二叉决策树算法-1.Fisher Classification Algorithm 2. Perceptron algorithm 3. Least-squares algorithm 4. Fast nearest neighbor 5.K-neighbor method 6. Clip
knn
- knn k近邻算法，可选择欧式距离或者曼哈顿距离-knn k nearest neighbor, Euclidean distance or Manhattan can choose the distance
2rar
- 用matlab写的最近邻和K近邻法分类器，简单易懂，适合初学者-Written with matlab and K-NN nearest neighbor classifier, easy to understand for beginners
ClassifyHomework
- 模式识别,用平均样本法、平均距离法、最近邻法、K近邻法进行分类。-Pattern recognition, with an average of the sample method, the average distance method, nearest neighbor, K-NN classification.
zuijinlinfenlei
- 我们使用MATLAB软件实现了人脸识别并统计其识别率。本实验采用PCA（主成分分析）方法，利用K-L变换和奇异值分解原理实现。并分别采用最近邻法分类器得出它们的成功率。-We use face recognition software and the MATLAB Statistics recognition rate. The present study, PCA (principal component analysis) meth
extendimage
- 根据模板大小对图像扩展，最近邻法，matlab-According to the template image size on expansion, k-nearest-neighbors, matlab
KKNNn
- Knn算法综述、柔性KNN算法研究、一个高效的knn分类算法法、一种改进的KNN分类算法、一种优化的K最近邻协同过滤算法。 -The Knn algorithm summarized flexible KNN algorithm, an efficient knn classification algorithm method, an improved KNN classification algorithm, an optimiz
1
- 利用K-L变换进行人脸识别。首先求得待辨识图像相对于训练集平均脸的差值图像，然后求得该图像在特征脸空间中的坐标，最后采用最近邻法对图像进行归类。-KL transform for face recognition. Obtain the first image to be identified image with respect to the difference between the average face of the tra
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- 最近邻法和K近邻法。作为分类器算法，k近邻法和最近邻法应用广泛-Nearest neighbor and K-nearest neighbor method. As a classifier algorithm, widely k nearest neighbor method nearest neighbor method and application
regress2beltak
- matlab实现使用统计学习基础中的最小二乘法和k-最近邻法进行分类。-matlab achieve statistical learning-based classification using the least squares method and k-nearest neighbor method.
classification-Python
- python实现感知器、贝叶斯分类、决策树分类、K最近邻法、逻辑回归、支持向量机-Python implementation of perceptron, Bias classification, decision tree classification, K nearest neighbor method, logic regression, support vector machine
jinlin
- 最近邻法，K-近邻法，剪辑近邻法和压缩近邻法。模式识别-Nearest neighbor, K- nearest neighbor, editing and compression nearest neighbor nearest neighbor method. Pattern Recognition
k-nearest-neighbors
- k最近邻法、有权重的k最近邻法及线性判别-K-nearest neighbor and linear discriminant analysis
类比法
- 型的类比学习方法是K-最近邻方法,它属于懒散学习法,相比决策树等急切学习法,具有训练时间短,但分类时间长的特点。K-最近邻算法可以用于分类和聚类中(The analogy learning method is K- nearest neighbor method. It belongs to the lazy learning method. Compared with the decision tree learning method
三步搜索法
- 本实验的目的是学习Parzen窗估计和k最近邻估计方法。在之前的模式识别研究中，我们假设概率密度函数的参数形式已知，即判别函数J(.)的参数是已知的。本节使用非参数化的方法来处理任意形式的概率分布而不必事先考虑概率密度的参数形式。在模式识别中有躲在令人感兴趣的非参数化方法，Parzen窗估计和k最近邻估计就是两种经典的估计法。(The purpose of this experiment is to study the Parzen w
KNN_demon
- 最近邻法语k近邻法的例子，基于matlab平台，有助于初学者学习。(The recent example of the nearest neighbour approach to French K, based on the MATLAB platform, helps beginners to learn.)