搜索资源列表
KMeanIntroduction
- 聚类分析是将集合中的数据按其相似性大小分成不同类别的一种方法,它是模式 识别中多变量无监督学习的一个分支,己成功地用于医学,地质,财务,工程,图像 处理和文档等的数据分类中；含有实现此算法的源码 -cluster analysis is to pool the data according to similar size into a different category, It is pattern recognition multivariable Unsupervised Le
人脸检测与语音驱动口型的文章
- 这是一篇详细介绍人脸检测与语音驱动口型的文章，其中使用了高斯混合模型采取了无监督聚类的方法，希望对你有用。,This is a detailed introduction Face Detection and voice-driven I-type article, which uses the Gaussian mixture model taken unsupervised clustering method, in the hope that useful to you.
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
Lecture15-2.Unsupervised-Learning-and-Clustering.
- Unsupervised Learning and Clustering
k-means
- Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines this reflects its
PatternRecognition_Ch5_Unsupervised-learning-and-
- This file is about unsupervised clustering
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
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.
video-key-frame-extraction
- 关键帧的提取方法的研究，对现有算法进行了总结分析，提出一种采用无监督聚类的自适应关键帧提取算法。-Keyframe extraction method, the existing algorithms were analyzed to propose a unsupervised clustering adaptive key frame extraction algorithm.
change-detectiones
- 关于变化检测的东西，很好!Fuzzy clustering algorithms for unsupervised change detection in remote sensing images-The Change Detection!Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Unsupervisededgedetection
- 单值图像在无监督情况下的边缘检测，是最新研究的热点-Single-valued image edge detection, unsupervised latest research hotspot
Unsupervised-segmentations
- Unsupervised segmentation of color-texture regions in images and video无监督的彩色图像分割方法，非常牛叉-Unsupervised segmentation of color-texture regions in images and video unsupervised color image segmentation method, is very cattle fork
image-study
- 多示例学习是与监督学习、非监督学习和强化学习并列的第四类学习框架，目前已广泛应用于药物设计、图像搜索等领域,并已获得很好的效果。在多示例学习中，训练样本是由多个示例组成的包，包是有概念标记的，但示例本身却没有概念标记，学习的目的是预测新包的类别。-Multi-instance learning and supervised learning, unsupervised learning and reinforcement learning tied for the fourth-class le
Introduction-to-Graphical-Model
- matlab语句实现图模型，多用于分析网络用户产品推荐及网络用户潜在社会关系分析模型的建立。-The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields , including computer science, engineering, mathematics, ph
Wyner-Ziv-Video-Coding
- The manuscr ipt describes the design of a new codec WZVC, where SI generation performed on unsupervised learning of two motion fields.
unsupervised-classification
- Clustering and Unsupervised Classification
Vassilaros_ISODATA
- a little detail on the method of ISODATA unsupervised classification
4Statistical-pattern-recognition
- The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used i
PMl-IR
- Blog信息源和信息量的广泛增长给中文文本分类带来了新的挑战。本文提出了—种基于PMI—IR算法的四种情感分类方法来对Blog文本进行情感分类。该方法以情感词语为中心，通过搜索引擎返回的结果来计算文本 中的情感要素和背景情感词之问的点互信息值，从而对文本进行情感分类。该方法在国家语言资源监测与研究中心网络媒体语言分中-心2008年度的Blog语料和COAE2008的语料上分别进行了测试。与传统方法相比准确率和召回率都有了较大的提高。-Development ofBIog texts info
Fully-Unsupervised-M-FISH
- Fully Unsupervised M-FISH Chromosome Image Characterization