文件名称:GibbsLDA
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- 上传时间：2014-10-01
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文件大小：6.26mb
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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
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下载文件列表
GibbsLDA：references/
GibbsLDA：references/1、An introduction to MCMC for machine learning.pdf
GibbsLDA：references/1、An introduction to MCMC for machine learning（原版）.pdf
GibbsLDA：references/2、Latent Dirichlet Allocation.pdf
GibbsLDA：references/3、A correlated topic model of Science.pdf
GibbsLDA：references/4、Gibbs sampling in the generative model of Latent Dirichlet Allocation.pdf
GibbsLDA：references/5、 Finding scientific topics——revisited.pdf
GibbsLDA：references/5、Finding scientific topics.pdf
GibbsLDA：references/5、Finding scientific topics.pptx
GibbsLDA：references/6、Parameter estimation for text analysis.pdf
GibbsLDA：references/7、Probabilistic latent semantic analysis.pdf
GibbsLDA：references/8、LDA-based document models for ad-hoc retrieval.pdf
GibbsLDA：references/GibbsLDA++-0.2/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/Makefile
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/README
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/docs/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/docs/GibbsLDA++Manual.pdf
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/docs/index.html
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.others
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.phi
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.tassign
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.theta
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.twords
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.others
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.phi
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.tassign
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.theta
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.twords
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/trndocs.dat
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/wordmap.txt
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/Makefile
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/constants.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/dataset.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/dataset.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/dataset.o
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/lda
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/lda.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/model.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/model.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/model.o
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/strtokenizer.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/strtokenizer.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/strtokenizer.o
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/utils.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/utils.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/utils.o
GibbsLDA：references/GibbsLDA++-0.2.tar.gz
GibbsLDA：references/1、An introduction to MCMC for machine learning.pdf
GibbsLDA：references/1、An introduction to MCMC for machine learning（原版）.pdf
GibbsLDA：references/2、Latent Dirichlet Allocation.pdf
GibbsLDA：references/3、A correlated topic model of Science.pdf
GibbsLDA：references/4、Gibbs sampling in the generative model of Latent Dirichlet Allocation.pdf
GibbsLDA：references/5、 Finding scientific topics——revisited.pdf
GibbsLDA：references/5、Finding scientific topics.pdf
GibbsLDA：references/5、Finding scientific topics.pptx
GibbsLDA：references/6、Parameter estimation for text analysis.pdf
GibbsLDA：references/7、Probabilistic latent semantic analysis.pdf
GibbsLDA：references/8、LDA-based document models for ad-hoc retrieval.pdf
GibbsLDA：references/GibbsLDA++-0.2/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/Makefile
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/README
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/docs/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/docs/GibbsLDA++Manual.pdf
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/docs/index.html
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.others
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.phi
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.tassign
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.theta
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/model-01800.twords
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.others
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.phi
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.tassign
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.theta
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/newdocs.dat.twords
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/trndocs.dat
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/models/casestudy/wordmap.txt
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/Makefile
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/constants.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/dataset.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/dataset.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/dataset.o
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/lda
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/lda.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/model.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/model.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/model.o
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/strtokenizer.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/strtokenizer.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/strtokenizer.o
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/utils.cpp
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/utils.h
GibbsLDA：references/GibbsLDA++-0.2/GibbsLDA++-0.2/src/utils.o
GibbsLDA：references/GibbsLDA++-0.2.tar.gz
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