搜索资源列表
Anity-Propagation
- 关于最新ap算法的开创性重要文章,应用数据分类-Affi nity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplar-based clustering. We present a deriva- tion of AP that is much simpler than the original one and is based on a qu
Pattern-Recognition-ppt
- 介绍模式识别的基本概念，详述了贝叶斯，参数估计，线性分类器，神经网络，随机方法，无监督学习与聚类等-Introduce the basic concepts of pattern recognition, Bayesian detailed, parameter estimation, linear classifiers, neural networks, stochastic methods, unsupervised learning and clustering, etc.
SOM
- SOM 是神经网络中很重要用C语言实现的算法。对于学习神经网络的人来说，可以学习学习。-SOM is an unsupervised neural network algorithm. The algorithm in the neural network has a significant impact. Learning neural network for people who are very important. I hope this code can give you help.
Kjunzhi
- VC实现K均值算法，达到对图像进行非监督分类的目的-VC to achieve K-means algorithm to the image the purpose of unsupervised classification
AutoUnsupervisedIimageClassification
- 无监督图像自动分类系统，我们在VC中做了一个基于聚类的图像分类方法，可以选择不同的图像特征，还有二个不同的显示方法。使用方法见“使用说明”。-Unsupervised image automatic classification system, we made a VC in the image classification method based on clustering, you can choose different image features, there are two dif
liuxinggaishu
- :流形学习是一种新的非监督学习方法,近年来引起越来越多机器学习和认知科学工作者的重视. 为了加深 对流形学习的认识和理解,该文由流形学习的拓扑学概念入手,追溯它的发展过程. 在明确流形学习的不同表示方 法后,针对几种主要的流形算法,分析它们各自的优势和不足,然后分别引用Isomap 和LL E 的应用示例. 结果表明, 流形学习较之于传统的线性降维方法,能够有效地发现非线性高维数据的本质维数,利于进行维数约简和数据分 析. 最后对流形学习未来的研究方向做出展望,以期进一步拓展流形
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
Kjunzhi
- K均值算法是遥感图像非监督分类里的重要算法-K-means algorithm is the unsupervised classification of remote sensing images in the important algorithm
GMM
- 无监督混合高斯模型（GMM）的EM估计，含两篇IEEE论文的源码-This is a set of MATLAB m-files implementing the mixture fitting algorithm described in the paper M. Figueiredo and A.K.Jain, "Unsupervised learning of finite mixture models", IEEE Transaction on Pattern Analys
jiyuISODATA
- 基于改进模糊ISODATA算法的遥感影像非监督聚类研究-Algorithm based on improved fuzzy ISODATA unsupervised clustering of remote sensing images
Unsupervised-optimal-FCM
- Fuzz c-mean Clusterin technique
K_Means
- 利用K均值聚类实现非监督分类的功能，可以将大部分常用格式图像进行分类，程序功能强大。-K-means clustering function of unsupervised classification, can be the most common format for image classification, and powerful program.
cppbgfg_gaussmix2
- 背景建模方法之高斯混合模型，使用到MOG2。算法快，并且可以进行阴影检测。遍历性：对每一个像素进行建模。作者为Z.Zivkovic-The algorithm similar to the standard Stauffer&Grimson algorithm with additional selection of the number of the Gaussian components based on: "Recursive unsupervised learning of fini
changedetectionpaper
- 关于遥感图像变化检测中基于无监督变化检测的的2篇英文论文-Two English papers on remote sensing image change detection based on unsupervised change detection
metric-learning_survey_v2
- 关于metric learning的综述，涉及到许多的知识：SVM、kernel、SDP等-This paper surveys the field of distance metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different
fcm
- 改进fcm分割算法，它是一种无监督分割方法，无需人的干预，分割过程完全是自动完成 它可以很好地处理噪声，部分体积影响和图像模糊。-Improve FCM segmentation algorithm, it is a kind of unsupervised segmentation method, without human intervention, process fully automatic segmentation complete It can be a very good de
shuzituxiangzuoye-
- 对遥感图像进行无监督分类，形成决策树，实现地物的标定-Unsupervised classification of remote sensing images to form a decision tree, the calibration of surface features
polsarpro_lecturecourse
- 雷达极化分类软件使用指导书，可以用于监督分类非监督分类。-Radar Polarimetric classification of software instructions can be used for unsupervised classification of the supervised classification.
TasoulisV2005b
- 非监督聚类的方法，该论文描述如何使用分形维数进行聚类判定准则，从而达到理想的聚类效果，经典论文-Unsupervised clustering method, the paper describes how to use the fractal dimension of the clustering criteria, in order to achieve the desired clustering effect, the classic paper