搜索资源列表
Knn1
- 模式匹配最近邻算法相关资料与Matlab源代码-Nearest-neighbor algorithm for pattern matching information with the Matlab source code
Knn3
- 模式匹配最近邻算法相关资料neib与Matlab源代码-Nearest-neighbor algorithm for pattern matching information neib with the Matlab source code
knn4
- 模式匹配最近邻算法相关资料Researchontheshapefeatureextractionandrecognition与Matlab源代码-Nearest-neighbor algorithm for pattern matching information Researchontheshapefeatureextractionandrecognition with the Matlab source code
classify
- 用感知器分类算法和最近邻分类算法对三类点进行分类，具有窗口界面，用鼠标点击就可以了，程序自动给出分类界面。-Classification algorithm with the sensor and the nearest neighbor classification algorithm to classify three categories of points, with the window interface, with a mouse click can be had, the pro
DCT
- 本文设计基于DCT的人脸识别系统，首先结合当今人脸识别的背景和发展状况讨论了人脸识别的研究内容及在各方面的应用；然后研究了人脸识别进行预处理，讨论了人脸识别预处理的其他方法，分析各种方法的利弊，最后采用DCT（离散余弦变换）实现人脸图像预处理中的降维处理；接下来对人脸图像的特征提取进行了研究，简单叙述了几何特征提取和代数特征提取，同时深入研究了基于DCT和PCA变换的人脸图像特征提取，从而实现是否对人脸识别系统识别率有所提高的研究；对于分类器的选择，本文对两种分类器进行了探讨，即最近邻分类器和B
chazhi
- 采用近邻插值和双线性插值两种插值方法，对图像进行放大处理。-Neighbor interpolation and bilinear interpolation using two kinds of interpolation methods to enlarge the image processing.
AI_Blood
- 本次大作业利用K‐近邻（K‐Nearest Neighbor）算法，为给定的训练数据集构造了分类器， 并在测试数据集上进行分类预测，同时计算了Accuracy、Precision、Recall和F‐measure，利用 10‐fold的实验方法进行交叉验证。-The big job to use K-neighbor (K-Nearest Neighbor) algorithm, for a given set of training data classifier is constru
nn_face
- nearest neighbor algorithm with orl database-nearest neighbor algorithm with orl database
SVM
- In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is in
knn_demo
- k近邻算法(knn, k nearest neighbor)-k nearest neighbor (knn, k nearest neighbor)
particle_neighbor_search_(latest_version)
- 介绍了如何在CUDA上搭建KD-TRIE，以及如何在CUDA上对其进行搜索和预处理，使其能适应解决粒子邻居搜索问题。-Describes how to build CUDA on KD-TRIE, and how to search them on CUDA and pre-treatment, so that it can adapt to solve the particle neighbor search problem.
ordinary_algorithm_for_pattern_recognition
- 使用C语言实现的一些简单模式识别聚类算法，用于简单的二维坐标系点的聚类。有最短距离算法、K均值算法、近邻算法、fcm算法、最大最小距离算法。-Using the C language implementation of some simple pattern recognition clustering algorithm for a simple two-dimensional coordinate system point of clustering. Has the shortest di
jcs44341-344
- The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient
Lancandiem1
- this file help us understand about neighbor operator! Easy to understand how to programimming with MFC! Good luck.
knn
- K=5的K近邻分类，训练样本集含有30个样本,矢量长度为5-K = 5 K-neighbor classification, the training sample set containing 30 samples, vector length of 5
knnsearch
- 寻找测试样本的最近邻，可以有效的用于用于模式识别，信号处理-This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing. The code was initially
cut_sample
- 使用二分法对样本集进行剪辑，剪辑法的思想，就是将样本集分成训练集与考试集， 利用训练集样本对考试集的样本进行分类（使用近邻法），如果考试集中某个样本分类错误的话，将这个样本删除。在该函数中，使用最近邻法，只进行一次剪辑（遍历完考试集中的样本以后退出）。还有一种重复剪辑法（适用于样本比较多的情况），把样本随机分为多个样本集，将相邻的两个样本前一个作为考试集，后一个作为训练集，调用二分剪辑。所有的样本子集剪辑完毕以后，在递归调用，直到没有样本被剪辑掉,没得讲，重复剪辑的效果肯定好一些。 -Th
20064817924orl_faces_112x92
- ORL人脸图像库，共40人，每人10幅图像，其中每人的前5幅作为训练样本，后5幅作为测试分类样本，统计正确分类率。分类准则为最近邻规则。 真实的图像尺寸为112x92，列向量堆积对应人脸库矩阵的每一列。 -ORL face image database, a total of 40 per 10 images, each of which the first five as training samples, after the 5 categories as a test sampl
FastNN
- 简单快速近邻算法：再二维平面一矩形范围内随机产生100个训练样本，任意划分为三个区域，用快速近邻法确定待识别实验样本的所属分类-Simple and rapid nearest neighbor: Another two-dimensional plane within a rectangle of 100 randomly generated training samples, arbitrary divided into three regions, with the fast neares
knniris
- 模式识别中的k近邻算法，用matlab编写，希望对大家有所帮助-K-nearest neighbor algorithm for pattern recognition with matlab write, we want to help