Description: 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 Bayes (Naive Bayes, NB) algorithm is commonly used in the field of machine learning a classification algorithm based on probability is very simple and effective. k neighbor method (k-Nearest Neighbor, kNN) [30,31], also known as case-based (Example-based, Instance-bases) of the algorithm, the basic idea quite intuitive: Rocchio law from the Information Retrieval System, and later was first proposed by the Hull in 1994, applies to classification [74], since then, Rocchio method of classification in the text with extensive application. Platform: |
Size: 96256 |
Author:许梁 |
Hits:
Description: 利用Sub-pattern PCA在Yale人脸库上进行人脸识别的matlab源代码，子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-pattern PCA use in the Yale face database for face recognition on the matlab source code, sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image Set the use of PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
Size: 2048 |
Author:章格 |
Hits:
Description: 子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image set to use PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
Size: 165888 |
Author:tanghui |
Hits:
Description: k最近邻分类器。编写的一个matlab函数。输入向量Z表示所属的类，返回一个向量，表示由k最近邻分类给定的。-k nearest neighbor classifier. Write a matlab function. Input vector Z that belongs to the class, returns a vector, expressed by the k nearest neighbor classification given. Platform: |
Size: 1024 |
Author:menghang |
Hits:
Description: In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor. Platform: |
Size: 589824 |
Author:bwindhya |
Hits:
Description: In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor. Platform: |
Size: 52224 |
Author:Putra |
Hits:
Description: In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor. Platform: |
Size: 1024 |
Author:aa |
Hits:
Description: 邻近算法，或者说K最近邻(kNN，k-NearestNeighbor)分类算法是数据挖掘分类技术中最简单的方法之一。所谓K最近邻，就是k个最近的邻居的意思，说的是每个样本都可以用它最接近的k个邻居来代表。
kNN算法的核心思想是如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性。该方法在确定分类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 kNN方法在类别决策时，只与极少量的相邻样本有关。由于kNN方法主要靠周围有限的邻近的样本，而不是靠判别类域的方法来确定所属类别的，因此对于类域的交叉或重叠较多的待分样本集来说，kNN方法较其他方法更为适合。-Nearby algorithm, or K-nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of classification data mining technology in the most simple way. The so-called K-nearest neighbor is the k nearest neighbors meant to say is that it can be used for each sample k nearest neighbors to represent. kNN algorithm core idea is that if a sample in feature space is k-nearest neighbor samples most belong to a category, the sample also fall into this category, and the category having the characteristics of the sample. The method in determining the classification decision based solely on the nearest one or several samples to determine the category to be sub-sample belongs to the category. kNN method when category decisions, with only a very small amount of adjacent samples related. Because kNN method is mainly limited by the surrounding adjacent samples, rather than the domain identification method to determine the class belongs to the category, so for class field of overlap or more s Platform: |
Size: 2048 |
Author:黑色地位 |
Hits:
Description: CNN(CondensedNearestNeighbor)是最早的基于近邻分类的实例选择算法。本程序实现了CNN算法，能很好的实现近邻分类的实例选择。-CNN (CondensedNearestNeighbor) is the earliest instance selection algorithm based on nearest neighbor classification. The core idea of the algorithm is that if the instance cannot be correctly classified by the currently selected set, the selected set is added. This program implements the CNN algorithm Platform: |
Size: 861184 |
Author:周鑫 |
Hits:
Description: KNN是通过测量不同特征值之间的距离进行分类。它的的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别。K通常是不大于20的整数。KNN算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。(NN is classified by measuring the distance between the different eigenvalues. It is the idea that the sample also belongs to this category if the majority of k samples of the sample in the feature space are the most similar (ie, the nearest neighbor in the feature space). K is usually an integer greater than 20. In the KNN algorithm, the selected neighbors are objects that have been correctly classified. The method determines the category of the sample to be sorted only on the classification decision based on the nearest one or several samples.) Platform: |
Size: 26624 |
Author:haibelief
|
Hits:
Description: ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen
instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of
these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle
is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast
gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance
to some well-established multi-label learning algorithms.
2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.) Platform: |
Size: 5120 |
Author:玖
|
Hits:
Description: 本程序主要参照论文，《基于OpenCV的脱机手写字符识别技术》实现了，对于手写阿拉伯数字的识别工作。识别工作分为三大步骤：预处理，特征提取，分类识别。预处理过程主要找到图像的ROI部分子图像并进行大小的归一化处理，特征提取将图像转化为特征向量，分类识别采用k-近邻分类方法进行分类处理，最后根据分类结果完成识别工作。
程序采用Microsoft Visual Studio 2010与OpenCV2.4.4在Windows 7-64位旗舰版系统下开发完成。并在Windows xp-32位系统下测试可用。(This procedure mainly refers to the paper, "OpenCV based offline handwritten character recognition technology" to achieve the recognition of handwritten Arabia digital work. The recognition work is divided into three major steps: preprocessing, feature extraction, classification and recognition. The pretreatment process is mainly to find ROI sub images of the image and normalized by the size of the feature extraction image into feature vector classification using k- nearest neighbor classification processing, according to the classification results to complete the identification work.
Procedures using Microsoft, Visual, Studio 2010 and OpenCV2.4.4 in Windows 7-64 bit ultimate system development completed. And in Windows xp-32 bit system test available.) Platform: |
Size: 23580672 |
Author:Kas_Zhao
|
Hits:
Description: 分别采用有边缘和无边缘两类图像作为样本库，使用用Hu不变矩对图像进行特征提取，并使用K近邻分类法进行分类和识别。(Two types of images with edges and no edges are used as the sample library, the images are extracted using Hu invariant moments, and the images are classified and identified by K nearest neighbor classification.) Platform: |
Size: 2048 |
Author:陆荏嘉
|
Hits:
Description: k-nn算法 K-NN算法 ( K Nearest Neighbor, K近邻算法 ), 是机器学习中的一个经典算法, 比较简单且容易理解. K-NN算法通过计算新数据与训练数据特征值之间的距离, 然后选取 K (K>=1) 个距离最近的邻居进行分类或者回归. 如果K = 1 , 那么新数据将被分配给其近邻的类.(k-nnK - NN algorithm (K on his Neighbor, K Nearest Neighbor algorithm), is a classical algorithm of machine learning, more simple and easy to understand. K - NN algorithm by calculating the new data and the distance between the training data characteristic value, and then select K (K > = 1) from the Nearest Neighbor classification or regression. If K = 1, then the new data will be class assigned to its neighbours.) Platform: |
Size: 1024 |
Author:smilephil |
Hits:
Description: 针对人脸特征分类问题，提出一种基于主动形状模型(ASM)和 K 近邻算法的人脸脸型分类方法。将 Hausdorff 距离作为 K 近邻算法的距离函数，利用 ASM 算法提取待测图像的特征点，对点集进行归一化后计算人脸轮廓特征点与样本库中所有样本点集的 Hausdorff距离，根据该距离值，通过 K 近邻算法实现待测图像的脸型分类。实验结果证明，该方法分类正确率高、速度快、易于实现。(Aiming at the problem of face feature classification, this paper proposes a new face classification algorithm based on Active Shape Model(ASM) and K-nearest neighbor algorithm. It extracts feature points of face by ASM algorithm, normalizes all feature points, and computes Hausdorff distance between feature points and every sample of each class. The face is classified by K-nearest neighbor algorithm with the Hausdorff distance computed. Experimental results show that the algorithm has high classification accuracy and speed, and it is easy to realize.) Platform: |
Size: 1341440 |
Author:夜湮 |
Hits:
Search - classification by nearest neighbor