文件名称:GibbsLDA
介绍说明－－下载内容均来自于网络，请自行研究使用
LDA算法，用于提取文字中的潜在类别，可以用于推荐个性化新闻之类的
-LDA algorithm used to extract the text of the latent class can be used to recommend personalized news and the like
-LDA algorithm used to extract the text of the latent class can be used to recommend personalized news and the like
(系统自动生成,下载前可以参看下载内容)
下载文件列表
GibbsLDA：references
....................\1、An introduction to MCMC for machine learning.pdf
....................\1、An introduction to MCMC for machine learning（原版）.pdf
....................\2、Latent Dirichlet Allocation.pdf
....................\3、A correlated topic model of Science.pdf
....................\4、Gibbs sampling in the generative model of Latent Dirichlet Allocation.pdf
....................\5、 Finding scientific topics——revisited.pdf
....................\5、Finding scientific topics.pdf
....................\5、Finding scientific topics.pptx
....................\6、Parameter estimation for text analysis.pdf
....................\7、Probabilistic latent semantic analysis.pdf
....................\8、LDA-based document models for ad-hoc retrieval.pdf
....................\GibbsLDA++-0.2
....................\..............\GibbsLDA++-0.2
....................\..............\..............\Makefile
....................\..............\..............\README
....................\..............\..............\docs
....................\..............\..............\....\GibbsLDA++Manual.pdf
....................\..............\..............\....\index.html
....................\..............\..............\models
....................\..............\..............\......\casestudy
....................\..............\..............\......\.........\model-01800.others
....................\..............\..............\......\.........\model-01800.phi
....................\..............\..............\......\.........\model-01800.tassign
....................\..............\..............\......\.........\model-01800.theta
....................\..............\..............\......\.........\model-01800.twords
....................\..............\..............\......\.........\newdocs.dat
....................\..............\..............\......\.........\newdocs.dat.others
....................\..............\..............\......\.........\newdocs.dat.phi
....................\..............\..............\......\.........\newdocs.dat.tassign
....................\..............\..............\......\.........\newdocs.dat.theta
....................\..............\..............\......\.........\newdocs.dat.twords
....................\..............\..............\......\.........\trndocs.dat
....................\..............\..............\......\.........\wordmap.txt
....................\..............\..............\src
....................\..............\..............\...\Makefile
....................\..............\..............\...\constants.h
....................\..............\..............\...\dataset.cpp
....................\..............\..............\...\dataset.h
....................\..............\..............\...\dataset.o
....................\..............\..............\...\lda
....................\..............\..............\...\lda.cpp
....................\..............\..............\...\model.cpp
....................\..............\..............\...\model.h
....................\..............\..............\...\model.o
....................\..............\..............\...\strtokenizer.cpp
....................\..............\..............\...\strtokenizer.h
....................\..............\..............\...\strtokenizer.o
....................\..............\..............\...\utils.cpp
....................\..............\..............\...\utils.h
....................\..............\..............\...\utils.o
....................\GibbsLDA++-0.2.tar.gz