搜索资源列表
Expectation-maximization
- 用IDL与ENVI 进行非监督分类的maximation算法，简单实用，功能强大-Use IDL and ENVI maximation non-supervised classification algorithms, simple, practical, powerful
SDA
- 半监督鉴别分析是一种很流行的算法，它利用了现实世界的大量的无标记的数据，并对它们分类-Semi-Supervised Discriminant Analysis (Graph Embedding Way)
Semi-Supervised_Learning_book
- Semi-Supervised Learning Edited by Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien 2006 Massachusetts Institute of Technology
(FuzzyARTMAP)1
- Adaptive resonance theory based neural network for supervised chemical pattern recognition
zkj
- 面对模式分析、数据挖掘中海量数据,降维算法已经成为科学研究人员最为 强有力的工具.对降维算法的研究具有很高的学术价值和应用潜力.本文较为详 细的回顾了现有的降维算法,以及他们在模式分析中的应用.在此基础上,着眼于 提高嵌入空间的不同类别的样本之间的距离,我们提出了两种有监督情形下的流 形学习算法.模拟和实际数据都显示了有监督流形学习算法的良好的性能.-Face pattern analysis, data mining massive data, dimension reduct
ssl_survey
- 本文回顾了半监督学习领域的各种算法，并陈述了该领域中的最近进展。-we review the literature on semi-supervised learning,which is an area in machine learning and more generally,artificial intelligence.
SemiL
- 利用基于图的分类方法， 半监督学习 ，分类软件。-SemiL is efficient software for solving large scale semi-supervised learning or transductive inference problems using graph based approaches.
s4vm
- s4vm算法，matlab 是实现半监督学习的较好的方法，能够对多种数据集进行测试，代码中包含例子，下载即可以使用-s4vm algorithm, matlab is better to achieve semi-supervised learning methods can be tested on a variety of data sets, the code contains examples that can be used to download
F-0358
- Semi-Supervised algorithm based on Fuzzy C-Means
ffc-1.4.tar
- Key Features * Neural network design, training, and simulation * Pattern recognition, clustering, and data-fitting tools * Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and laye
dtkirsch-hmm-v0.2.0-0-g7feffa1
- 这是一个Ruby机器学习项目中本地实现广义隐马尔可夫模型的分类。目前，它能够监督学习和Viterbi解码。-This project is a Ruby gem ( hmm ) for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier. At present, it is capable of supervised learning (using la
meanS3VM
- MissSVM是一揽子解决多实例使用半监督支持向量机的学习问题。MissSVM目的是显示，如果假设IID实例，多实例学习可以作为一个半监督学习的特殊情况来看，可能会合并成半的领域和多实例学习领域监督学习。 因此，未来的多实例学习研究应只承担IID袋，避免IID实例假设。 -MissSVM package solution is to use multi-instance semi-supervised support vector machine learning problems. MissS
SVM
- A support vector machine (SVM) is a concept in statistics and computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis.
Neural-Network-Classifiers
- Fast implementation of the GRLVQ, SRNG and H2MGLVQ algorithms, three supervised LVQ classifiers, run mexme_NN to recompile mex files on your own plateform, run test_NN.m to run demo.
s4vm
- 该软件包包括半监督算法S4VM，对无标签数据不产生衰退现象，或安全半监督算法的MATLAB代码。-The package includes the MATLAB code of the semi-supervised algorithm S4VM, which towards making unlabeled data never hurt, or safe semi-supervised algorithm.
sap
- 使用有监督的近邻传播聚类算法进行特征波段选取-Neighbors using a supervised clustering algorithm for propagation characteristics of selected bands
supervised_classification
- 模式识别中的监督分类程序，通过在遥感影像上选择训练样区，计算混淆矩阵，进行分类。-Pattern recognition supervised classification procedures, through training in remote sensing image, select the sample area, calculate the confusion matrix, classification.
S4VM
- S4VM 对传统的S3VM 进行了改进。传统的S3VM 基于低密度假设，它试图找到一个低密度的分界线， 也就是更倾向于决策边界穿过特征空间的低密度区域。S4VM 和S3VM 的不同点在于，S3VM 试图把注意力 集中在一个最优的低密度分界线上，而S4VM 则同时关注多个可能的低密度分界线。之所以这样做，是因为 给定一些有标记的点和大量未标记的点，可能存在着不止一个“间隔”较大的低密度分界线（如图2 ），基于 有限的标记样本，很难决定哪个是最好的。虽
jiandufenlei
- 遥感数据简单的监督分类程序代码，遥感数据简单的监督分类程序代码-Supervised classification of remote sensing data in a simple code
wekaUT.tar
- 实现半监督聚类，针对weka框架进行扩展。-It realize semi-supervised clustering method. And it is extension of weka.