{"id":1367,"date":"2014-12-02T16:29:56","date_gmt":"2014-12-02T08:29:56","guid":{"rendered":"http:\/\/blog.dayandcarrot.net\/?p=1367"},"modified":"2014-12-02T16:29:56","modified_gmt":"2014-12-02T08:29:56","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%9b%b8%e5%85%b3%e7%9a%84%e4%b8%80%e4%ba%9b%e4%b8%ad%e6%96%87%e6%95%99%e7%a8%8bmachine-learning-for-graphics-vision-and-multimedia","status":"publish","type":"post","link":"https:\/\/dayandcarrot.space\/?p=1367","title":{"rendered":"[\u673a\u5668\u5b66\u4e60\u76f8\u5173\u7684\u4e00\u4e9b\u4e2d\u6587\u6559\u7a0b]Machine Learning for Graphics, Vision and Multimedia"},"content":{"rendered":"

\u627e\u5230\u4e86\u4e00\u4e2a\u6bd4\u8f83\u597d\u7684\u7f51\u7ad9\uff0c\u91cc\u9762\u662f\u4e00\u4e9b[\u673a\u5668\u5b66\u4e60\u76f8\u5173\u7684\u6559\u7a0b\uff0c\u91cd\u8981\u7684\u662f\u90fd\u662f\u4e2d\u6587\u7684\uff0c\u8bfb\u8d77\u6765\u6bd4\u8f83\u5feb\u3002
\nHomepage: http:\/\/www.cmlab.csie.ntu.edu.tw\/~cyy\/learning\/<\/a><\/p>\n

Topics<\/h2>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Date<\/b><\/td>\nTopic<\/b><\/td>\nTutorial<\/b><\/span><\/td>\nReferences<\/b><\/td>\n<\/tr>\n

<\/p>\n

03\/16<\/td>\nPrincipal Component Analysis<\/a>
\n\u59dc\u4efb\u9060 \u6587\u5b97\u9e9f<\/td>\n		\nPCA<\/a><\/td>\n\n
03\/23<\/td>\nPCA Extensions<\/a>
\n\u8449\u51a0\u9e9f
\n\u9ec3\u8f14\u4e2d<\/td>\n		\nPCA missing data<\/a>
\nRobust PCA<\/a><\/td>\n		\n
03\/30<\/td>\nIsomap
\nLocally Linear Embedding<\/a>
\n\u8b1d\u660c\u71b9 \u8a31\u5e73<\/td>\n		\nISOMAP & LLE<\/a><\/td>\n\n
04\/06<\/td>\nLaplacian Eigenmaps<\/a>
\nLinear Discriminant Analysis<\/a>
\n\u9ec3\u4fca\u7fd4 \u9673\u99ff\u4e1e \u856d\u6df3\u6fa4<\/td>\n		\nLDA<\/a>
\nLDA applications<\/a><\/td>\n		\n
04\/13<\/td>\nLocality Preserving Projection
\nLocal Discriminant Embedding<\/a>
\n\u856d\u5fd7\u5091 \u7fc1\u4ef2\u6bc5
\n\u6f58\u632f\u9298<\/td>\n		\nLPP<\/a><\/td>\n\n
04\/27
\n05\/04<\/td>\n		Support Vector Machines<\/a>
\n\u674e\u6839\u9038
\n\u6797\u5b97\u52f3
\n\u6797\u5b8f\u5112
\n\u4f55\u6607\u822b<\/td>\n		\nSVM<\/a>
\nSVM<\/a>
\nSVM<\/a>
\nlibsvm<\/a><\/td>\n		\n
<\/td>\nSupport Vector Regression<\/a>
\n\u9ec3\u5b50\u6853<\/td>\n		SVR<\/a><\/td>\n\n
<\/td>\nRelevance Vector Machine<\/a>
\n\u694a\u5584\u8a60<\/td>\n		RVM<\/a><\/td>\n\n
05\/11<\/td>\nBoosting<\/a>
\n\u7fc1\u660e\u6609
\n\u9673\u5b8f\u6690
\n\u9ec3\u4fe1\u9a2b
\n\u912d\u93a7\u5c39<\/td>\n		\nEnsemble Learning<\/a>
\nAdaBoost binary<\/a>
\nAdaBoost extensions<\/a>
\nAdaBoost applications<\/a><\/td>\n		\n
05\/18
\n05\/25<\/td>\n		Graphical Models
\n\u6731\u5a01\u9054<\/td>\n		\nGraphical Model<\/a><\/td>\n\n
<\/td>\nBelief Propagation<\/a>
\n\u912d\u6587\u7687 \u8b1d\u81f4\u4ec1
\n\u8b1d\u6c38\u6853<\/td>\n		\nlow-level learning<\/a>
\nApplications<\/a><\/td>\n		\n
05\/25
\n06\/01
\n06\/08<\/td>\n		Approximate Inference<\/td>\n<\/td>\n\n
<\/td>\nExpectation Maximization<\/a>
\n\u838a\u4e0a\u5880 \u90ed\u715c\u6953 \u5289\u6cbb\u6770 \u694a\u6055\u5148<\/td>\n		\nEM<\/a><\/td>\n\n
<\/td>\nVariational Learning<\/a>
\n\u6797\u84c9\u73ca
\n\u5468\u4eae\u745c
\n\u5442\u65fa\u6d32<\/td>\n		\nVariational Learning<\/a>
\nVariational Learning<\/a>
\nVariational Learning<\/a><\/td>\n		\n