搜索资源列表
Myknn
- knn，即k最近邻算法是模式识别中的一种比较简单而经典的分类算法-Knn, or k-nearest neighbor algrithom, is a simple and classical classifier algrithom.
knn
- 8个采样点的k近邻算法,结果用语言表示 两个类别
KNN
- K近邻算法（KNN）的matlab源代码，程序清晰易读-K nearest neighbor (KNN) of matlab source code, procedures legible
knn
- knn (k-nearest neighbor)用c++实现的近邻算法-knn (k-nearest neighbor) algorithm
knn_demo
- k近邻算法(knn, k nearest neighbor)-k nearest neighbor (knn, k nearest neighbor)
k-means
- K近邻算法，对一段数据进行分类，word 说明文档-K nearest neighbor algorithm
K-nearest
- 关于K近邻算法的详细描述，包括算法原理及应用背景。-K-nearest neighbor algorithm on a detailed descr iption, including algorithm theory and application background.
K-negibour-method
- 利用K近邻法实现数字识别算法。误差小，识别效率高，网络训练速度快。-K-nearest neighbor algorithm, digital identification algorithm. Error is small, high recognition efficiency and speed of network training.
KNN
- 模式识别大作业K近邻算法（KNN）C++实现，内有iris和wine数据测试以及其他相关资料。-, Pattern Recognition large job K nearest neighbor algorithm (KNN) C++ achieved within the iris and wine data testing and other relevant information.
kNN
- 用matlab实现K近邻算法，用于数据挖掘的分类-K-nearest neighbor algorithm for the classification of data mining using matlab
K---nearest-neighbour-classifier
- 采用快速K近邻与Kmeans聚类算法来计算前K个近邻，舍弃了一部分不可能成为待测样本的前K个近邻的训练样本，从而减少了计算量，提高了分类速度-Fast K-nearest neighbor Kmeans clustering algorithm to calculate the K nearest neighbors, abandoning the training samples of the part can not become the first K neighbors of the t
wine_class
- 借助于k-近邻算法，实现对酒品质的鉴定。并在算法中采用k值的调整以求结果更优。-By means of a k-nearest neighbor algorithm, identification of the quality of the wine. And k values used in the algorithm to adjust in order to better results.
ncm.v1.1
- k近邻算法源代码，用matlab语言编写的，适用于分类及相关问题的分析-K neighbor algorithm source code, written with matlab language, suitable for classification and analysis of the related problem
KNN
- 《机器学习实战》K近邻算法的代码实现以及数据，真实可靠，欢迎使用-" Machine learning real" K nearest neighbor algorithm code and data, real reliable, welcome
python-code-for-Machine-learning
- 用于机器学习的全方位python代码，包括K-近邻算法、决策树、朴素贝叶斯、Logistic 回归 、支持向量机、利用 AdaBoost 元算法提高分类性能、预测数值型数据：回归、树回归、利用 K-均值聚类算法对未标注数据分组、使用 Apriori 算法进行关联分析、使用 FP-growth 算法来高效分析频繁项集、利用 PCA 来简化数据、利用 SVD 简化数据、大数据与 MapReduce-The full range of python code for machine learning
kNN
- 机器学习实战中，K近邻算法的实现。包括算法实现，算法分类测试-Machine learning combat, the realization of K nearest neighbor algorithm. Including the algorithm, the algorithm classification test
NNC
- K近邻算法，亲测，可运行，手动输入近邻个数(The nearest neighbor algorithm)
kNN
- 通过k近邻算法实现数字识别，主要包含0-9之间的所有数字。(Digital identification is realized by K nearest neighbor algorithm.)
MLInActionCode-master
- 机器学习实战的源代码集合，第一部分主要介绍机器学习基础，以及如何利用算法进行分类，并逐步介绍了多种经典的监督学习算法，如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树（CART）算法等。第三部分则重点介绍无监督学习及其一些主要算法：k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具(Machine learning combat source code collection
KNN python
- 关于K近邻算法的简单实现和一些例子，其中包括手写数字的识别(Simple implementation of K nearest neighbor algorithm and some examples)