搜索资源列表
Isodata
- 这是一个关于遥感图像非监督分类法的算法，希望对大家有所帮助-This is a remote sensing image on the unsupervised classification algorithm, we want to help
EM-algorithm
- EM算法，是一种无监督的聚类算法，可以实现对数据的处理，对不同数据进行聚类，生成类内相似度最大-EM algorithm is an unsupervised clustering algorithm, the data processing can be achieved on different data clustering, to generate the maximum within-class similarity
shang
- 一种无监督的数据离散化方法，程序简单，运行时间短，效果比较显著-An unsupervised data discretization methods, procedures easy, run a short time, compared the effect of significantly
ANNPID
- 分别采用4种控制律进行单神经元PID控制,即无监督的Hebb学习规则、有监督的Delta学习规则、有监督的Hebb学习规则、改进的Hebb学习规则.-Separately using four kinds of control laws for single neuron PID control, that is, unsupervised Hebb learning rules, there is the Delta Study supervision rules, there is supe
TextClustering
- 文本聚类，VC编程实现，作为一种无监督的机器学习方法，聚类由于不需要训练过程，以及不需要预先对文档手工标注类别，因此具有一定的灵活性和较高的自动化处理能力-Text Clustering, VC programming, as an unsupervised machine learning method, clustering by eliminating the need for the training process, and do not need to manually pre-ma
isodata
- isodata是个重要的非监督聚类算法，本文件提供了isodata的c++描述-isodata is an important unsupervised clustering algorithm, this paper provides a isodata of c++ descr iption
GMM_Purdue
- 基于混合高斯模型（GMM）的无监督聚类算法，希望对大家有帮助-Based on Gaussian mixture model (GMM) unsupervised clustering algorithm, I hope it would have help to you！
program
- 基于无监督学习的谱聚类算法的文本的聚类分类。-Unsupervised Learning Based on spectral clustering algorithm for text clustering classification.
GMM-GMR-v2.0
- In statistics, a mixture model is a probabilistic model for density estimation using a mixture distribution. A mixture model can be regarded as a type of unsupervised learning or clustering. Mixture models should not be confused with models for compo
ISODATA
- 模式识别非监督法分类ISODATA算法,对待识别样本进行自动分类.特征向量取2纬.-Pattern recognition algorithm for unsupervised ISODATA classification method
Demo1_ICM
- demo for icm algorithm
12439874
- SAR图像的极化干涉非监督Wishart分类方法和实验研究-Polarimetric Interferometric SAR image classification Unsupervised Wishart and Experimental Research
63
- 非监督判别训练 希望对大家有用 这是一个很好的英文文献-Unsupervised Discriminant Training
ctex
- In this project, we intend to segment natural images by combing colour and texture information. For this we will be using an unsupervised image segmentation framework (referred to as CTex) that is based on the adaptive inclusion of color and texture
GimagepatterclassfiyVC
- 成功完成了模式识别的监督和非监督方法包括：Fisher线性分类器，分级聚类算法等经典算法，程序易懂。-Successfully completed the supervision and unsupervised pattern recognition methods include: Fisher linear classifier, hierarchical clustering algorithms such as classical algorithm, the program easy
UnsupervisedAnomalyDetectionBasedOnPrincipalCompon
- 入侵检测系统在训练过程中需要大量有标识的监督数据进行学习,不利于其应用和推广.为了解决该问题,提出了一种基于主成分分析的无监督异常检测方法,在最小均方误差原则下学习样本的主要特征,经过压缩和还原的互逆过程后能最大限度地复制样本信息,从而根据均方误差的差异检测出异常信息.构建的仿真系统经过实验证明,基于主成分分析的无监督异常检测方法能够在无需专家前期参与的情况下检测出入侵,实验结果验证了其有效性.-Intrusion Detection System in the training process
Unsupervised_Adapting_in_Speech_Recognising_using_
- 介绍了一种基于词网的最大似然线性回归无监督自适应算法，并进行了改进。根据解码得到的词网估计变换参数，词网的潜在误识率远小于识别结果，因此可以使参数估计更为准确。传统的一个很大缺点是计算量极大，较难实用，对此本文提出了两个改进技术：1利用后验概率压缩词网；2利用单词的时间信息限制状态统计量的计算范围。实验测定,误识率比传统相对下降了。-Introduced the term network based maximum likelihood linear regression unsupervise
Spedaker_Adapting_in_Speech_recognizing
- ：自适应技术在近年来得到越来越多的重视，其中应用广泛的包括,-.、,//0，该技术利用少量特定 人数据就可以调整码本，快速地提升识别性能，它要求原始的码本有很好的说话人无关性。本文介绍了结合 ,//0 自适应的说话人自适应训练（1234536 -74289:3 649<9<=，以下简称1- ）算法，这种方法将每个说话人码本 视为说话人无关码本经过线性变换的结果，在此基础上训练的说话人无关码本更有效剔除了说话人相关信 息，因此在说话人自适应中时能根据特定数据调整更好地逼
wujianduxuexi
- 无监督学习与聚类课件，介绍了无监督学习算法-unsupervised clustering
EM-GMM
- Em algo for GMM, data mining unsupervised learning-Em algo for GMM, data mining unsupervised learning