搜索资源列表
KNNre
- JAVA写的K最近邻算法的基本实现，可以调试通过。-KNN algorithm using JAVA.
123
- 该程序实现K-均值聚类算法达到K-均值聚类的功能，与凝聚算法 最近邻聚类算法达到最邻聚类的功能。 -The program implements K- K- means clustering algorithm to achieve functional means clustering, and cohesion algorithm- nearest neighbor clustering algorithm to achieve the most-neighbor clustering.
kjinlin
- K最近邻(k-Nearest Neighbour，KNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。该方法的思路是：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 neighbor algorithm
WekaKnnInJAVA-master
- weka knn java算法实例，K-最近邻算法分析测试-weka knn java api
Parzen_k
- 主要内容包括两种非参数估计方法：Parzen窗估计和k最近邻估计。-The main contents include two non parametric estimation methods: Parzen window estimation and K nearest neighbor estimation.
KraskovMI
- 计算Kraskov估计的互信息值。首先计算各样本的最近邻，然后给出两种互信息值。-computes the Kraskov estimator for the mutual information.k: nearest neighbour. Output: I1, I2: the two estimator of MI.
kUntitled1
- k-最近邻算法分类器，程序清晰易读，有注解，方便最算法的进一步掌握-K- nearest neighbor classifier algorithm, procedures Notes clear and easy to read, easy to grasp the algorithm.
KNN
- 自己实现机器学习十大算法中的k最近邻算法，经过测试，算法运行很好-Own machine learning algorithm to achieve the k nearest neighbor algorithm, tested, the algorithm runs very well
cedbk
- 基于K均值的PSO聚类算法,非常适合计算机视觉方面的研究使用,包括最小二乘法、SVM、神经网络、1_k近邻法。- K-means clustering algorithm based on the PSO, Very suitable for the study using computer vision, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
knn-kdtree
- kd树，分割k维数据空间的数据结构。主要应用于多维空间关键数据的搜索（如：范围搜索和最近邻搜索）。K-D树是二进制空间分割树的特殊的情况。-KD tree, the data structure of K dimensional data space. It is mainly used in the search of key data in multidimensional space (such as range search and nearest neighbor search). K
KNN-python
- 邻近算法，或者说K最近邻分类算法是数据挖掘分类技术中最简单的方法之一，给出一个实例，可直接运行- U90BB u8B1 u7B97 u7B97 u7C97 u6c2 U6700 u7B80 u5355 u7684 u65B9 u6CD5 u4E4B u4E00 uFF0C u7ED9 u51FA u4E00 u4E2A u5B9E u4F8B uFF0C u53EF u76F4 u63A5 u8FD0 u884C
knn
- 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。(Neighborhood algorithm, or K nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of the simplest methods in data mining class
knn
- 运用java 语言简单实现knn算法，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(Using java language simple implementation of KNN algorithm, neighbor algorithm, or K nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of the simples
KNN
- 最近邻学习算法，Python实现，最近邻规则分类(steps: In order to determine the unknown instance categories, with examples of all known categories as reference Parameter selection of K The calculation examples and all known examples of the unknown distance Choose the
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, it has the characteristics o
MachineLearning-master
- 机器学习算法，包括knn等，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。(machine learning algorithm)
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 window estimation and the k nea
knn
- k最近邻算法:分类和回归。通过对训练集分类训练模型，验证集用于验证数据的准确性。(K nearest neighbor algorithm: classification and regression. Through the training set classification training model, the verification set is used to verify the accuracy of the data.)
KNN方法
- 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(The adjacent algorithm, or the K nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of the simplest methods in the data mining classification technique)
KNN人脸识别
- 使用KNN算法实现的人脸识别程序，KNN是机器学习里的K最近邻算法。(face recognition of KNN using python)