搜索资源列表
1
- 基于WEKA平台的文本聚类研究与实现 文本聚类是文本挖掘领域的一个重要研究分支，是聚类方法在文本处理领域的应用。本文对基于空间向量模型的文本聚类过程做了较深入的讨论和总结，利用文本语料库，基于数据挖掘工具研究并实现了文本聚类的过程。本文首先给出了文本聚类的思想和过程，回顾了文本聚类领域的已有成果，列举了文本聚类领域在特征表示、特征提取等方面的基础研究工作。另外，本文回顾了现有的文本聚类算法，以及常用的文本聚类效果评价指标。在研究了已有成果的基础上，本文利用20 Newsgroup文本语料库，
e4k-means-althogrim
- 基于K—Means的中文文本聚类算法的研究和实现。中文文本聚类的主要技术，特征选择，共享最近邻的K-Means的改进算法。基于k-Means的实现和实验。-Based on the Chinese version of K-Means clustering algorithm and implementation. The main technology of Chinese Text Clustering, feature selection, shared nearest neighbor
Texturefeatureselect
- 摘 要: 本文基于Landsat7ETM+全色图像,以浙江省临安市大峡谷镇为研究区,进行基于遗传算法的遥感图像纹理特征选择的研究-Abstract: This article is based Landsat7ETM+ panchromatic image to the Grand Canyon Town, Lin' an City, Zhejiang Province, for the study area, genetic algorithm-based remote sensing
ga
- 基于遗传算法和距离的特征选择，用于人工智能和模式识别，进行降维-Distance based on genetic algorithms and feature selection for artificial intelligence and pattern recognition, dimensionality reduction
PCA-and-MatlabCode
- 主成分分析法实现特征选择，里面有非常详细的算法介绍，还有一个现实实例介绍，非常具象，里面贴有实现算法的matlab代码-Principal component analysis （PCA）to achieve feature selection , which has a very detailed descr iption of algorithms , there is a real example to illustrate , very concrete , which is affix
A-Survey-of-Feature-Selection
- 对特征选择方法的发展历史和现状进行了跨学科的广泛调研，在此基础上总结提出了通用的方法定义和算法流程框架-A unifring approach to the definition of the feature selection problem and corresponding algorithm design framework are proposed based on a complete survey of a wide range of interdisciplinary res
RBF_OLS
- 利用RBF神经网络通过剪枝算法实现特征选择，训练时采用正交最小二乘方法-RBF feature selection algorithm with gradient decending method
pso_featsel2
- 这是一个粒子群算法（PSO）用于特征选择的matlab函数，用户可以自由选择目标函数优化方向、种群大小、迭代次数等等。-this is an feature selection program with particle swarm optimization of matlab ,you can chose the Function optimization direction the scale of the swarm and the number of the iteration
qpso_featsel2
- 这是一个基于量子行为的粒子群算法（QPSO）用于特征选择的matlab程序，用户可以选择目标函数优化方向、种群大小，也可以在内部修改QPSO的参数beta-this is a feature selection program based on quantum_behaved particle swarm optimization of matlab ,the user can chose the object function optimization direction,the scale
Pattern-Recognition_MATLAB_1
- 线性分类器设计，1_k近邻法，剪辑法，ANN-BP神经网络法，聚类分析法，特征选择，特征提取等模式识别常用算法，内有matlab可运行实现，还有word文档的说明。对于学习，是做好的资料。-Commonly used algorithm for linear classifier design the 1_k nearest neighbor, clip, ANN-BP neural network method, cluster analysis, feature selection, fea
spider
- 机器学习matlab源代码，包括多分类SVM，模式识别，特征选择，回归等算法。-The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with
ga1
- 遗传算法用于模式分类的特征选择。以minstall数据库为例，介绍了详细应用例程。-Genetic algorithm for pattern classification feature selection. Minstall database, for example, describes in detail the application routines.
MainEigenvectorsSelectionInMIMO
- 该资料给出了一种基于特征空间的MIMO天线选择算法，并给出了详细的算法描述，及仿真结果。-The information gives a antenna selection based on main eig envectors of CSI, and gives a detailed descr iption of the algorithm, and simulation results
AdaBoost_TrainData
- 这份代码是使用haar+adoboost做人脸检测用的，文件中还包括其他的特征选择，特征提取的算法，对于想做人脸检测这一块的朋友，可以下载来研究。-This code is used haar+adoboost human face detection, the document also includes other feature selection, feature extraction algorithm for face detection which one wants a frie
Feature-selection-
- 基于基因遗传算法的特征选择程序 高喊制图功能,直接给出匹配耦合-Feature selection based on GA in Matlab
Oil-spill-detection
- 基于SAR的溢油检测算法，对特征提取并进行特征选择。-Oil spill detection algorithm based on SAR, the feature extraction and feature selection.
Single-Pass
- 改进Single-Pass聚类算法,包括分词、tfidf计算、卡方检验特征选择-Improved Single-Pass Clustering Algorithm
GA
- matlab中编的基于遗传算法的转子故障特征选择的小程序，新手程度，仅供参考-matlab in the series based on genetic algorithm rotor fault feature selection of small programs, novice level, for reference only
csvs
- 基于改进差分演化算法的石油层评价指标的特征选择-Feature improved oil layer evaluation based on differential evolution algorithm choice
PSO_TSP
- 利用PSO算法解决下面问题： （1） 函数优化； （2） TSP问题； （3） 属性特征选择；-Use PSO algorithm to solve the following problems: (1) Function Optimization (2) TSP problem (3) property feature selection