搜索资源列表
ICA_demo_text
- ICA is used to classify text in extension to the latent semantic indexing framework. ICA show to align the context grouping structure well in a human sense [1], thus can be used for unsupervised classification. The demonstration shows this on medical
pLSA_demo
- plsa-demo, probabilistic Latent Semantic Analysis. A very important code in object recognition
plsa
- Probabilistic Latent Semantic Analysis模型实现，用于目标识别或文本识别
LSA
- 用于文本语义分析的潜在语义分析算法LSA(Latent Semantic Analysis)，包含详细的函数说明和原理分析
pLSA
- 用于文本分析的pLSA(Probability Latent Semantic Analysis)的Matlab算法，含有测试数据及算法原理介绍。也可用于图像分析。
基于贝叶斯网络的半监督聚类集成模型
- 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca
fast Gaussian process latent variable model
- fast Gaussian process latent variable model Software (FGPLVM). This toolbox allows for larger GP-LVM models through using the sparse approximations suggested in papers by authors including Titsias, Snelson, Ghahramani, Seeger, and Lawrence
LDA.rar
- LDA的c语言实现，LDA是一个针对文档的分层概率模型。,This is a C implementation of latent Dirichlet allocation (LDA), a model of discrete data . LDA is a hierarchical probabilistic model of documents.
LDA 算法的C++实现
- Latent Dirichlet Allocation算法的C++实现-Algorithm for Latent Dirichlet Allocation of C++
lda-j-src-20050325
- LDA (latent dirichlet allocation) 的java实现-The java code for LDA (latent dirichlet allocation)
EM
- EM算法Matlab实现。最大期望（EM）算法是在概率（probabilistic）模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐藏变量（Latent Variable）-EM algorithm by Matlab. Maximum expected (EM) algorithm is probabilistic (probabilistic) model to find maximum likelihood parameter estimation or m
Query
- 根据潜在语义分析进行查询。将文本中的特征集合做LSI变换。-Latent Semantic Analysis in accordance with inquiries. The text of the characteristics of a collection so LSI transformation.
lda-c-dist
- latent dirichlet allocation C语言实现算法 LINUX 下运行-latent dirichlet allocation the c implementation
PLStextclass
- 基于PLS的文本分类技术研究，和潜在语义索引联系密切，研究文本分类中特征抽取的重要参考。-PLS-based text classification technology, and closely linked to latent semantic indexing, feature extraction of text classification an important reference.
lda-c-dist
- latent dirichlet allocation论文作者的算法代码，采用纯C实现，在LINUX下运行成功。-latent dirichlet allocation algorithm of the code authors, using pure C implementation, running under the LINUX success.
CLDA
- Latent Dirichlet Allocation程序，基于EM算法。能得到alpha和beta参数-Latent Dirichlet Allocation program, based on the EM algorithm. Alpha and beta parameters can be derived
ObjectLocalization_Code
- 一个基于Felzenszwalb的latent svm的目标检测框架-This is an implementation of our object localization system as described in [1]. This system is an adaption of the object detection framework of Felzenszwalb et al. [2][3](http://people.cs.uchicago.edu/~pff/latent-r
Latent-Dirichlet-Allocation-2003
- 推荐算法,Latent Dirichlet Allocation -recommendation system
Latent-LRR
- 由文章作者提供的隐式低秩子空间聚类算法。参考文献：Guangcan Liu, Shuicheng Yan. Latent low rank representation [J]. Springer International Publishing, 2014:23-38. -The program is provided by the authors to realize latent low rank subspace clustering. Reference: Guangcan Liu,
Multi-View Clustering in Latent Embedding Space
- Multi-View Clustering in Latent Embedding Space