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\ub525\ub7ec\ub2dd(Deep Learning) \uae30\uc220\uc774 \uc811\ubaa9\ub418\uba74\uc11c \ud2b8\ub808\uc774\ub529\uc758 \ud6a8\uc728\uc131\uc774 \ub354\uc6b1 \ub192\uc544\uc84c\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uae30\ubc95\uacfc \ubcc0\ub3d9\uc131 \uc9c0\ud45c\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uae4a\uc740 \ub0b4\uc6a9\uc744 \ub2e4\ub8e8\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 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\uae30\ubc18\uc758 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc740 \ubc29\ub300\ud55c \uc591\uc758 \ub370\uc774\ud130\ub97c \ube60\ub974\uace0 \uc815\ud655\ud558\uac8c \ubd84\uc11d\ud558\uc5ec \ucd5c\uc801\uc758 \ub9e4\ub9e4 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uac1c\ub150\uacfc \uc6d0\ub9ac,\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 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<\/p>\n

\ucd5c\uadfc \uba87 \ub144 \uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc778\uae30\ub294 \uae09\uc99d\ud588\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub370\uc774\ud130 \uacfc\ud559 \ubc0f \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uadf8 \uc911\uc2ec\uc5d0 \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \uc774\ub97c \uc774\uc6a9\ud55c \ud000\ud2b8 \ub9e4\ub9e4\uc758 \uc774\uc810\uc744 \ub17c\uc758\ud558\uba70, \ub9c8\uc9c0\ub9c9\uc73c\ub85c scikit-learn<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud55c PLSA(Probabilistic Latent Semantic Analysis)<\/strong>\uc758 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uacfc \uba38\uc2e0\ub7ec\ub2dd<\/h2>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc5d0 \ub530\ub77c \ub9e4\ub9e4\ub97c \uc790\ub3d9\uc73c\ub85c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \uc5ec\ub7ec \ubcf5\uc7a1\ud55c \uacb0\uc815 \uacfc\uc815\uc744 \uc218\ud559\uc801 \ubaa8\ub378\uc774\ub098 \uae30\uacc4 \ud559\uc2b5 \ubaa8\ub378\ub85c \ub9cc\ub4e4\uc5b4 \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc2e4\ud589\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc811\uadfc \ubc29\uc2dd \ub355\ubd84\uc5d0 \uac10\uc815\uc801 \uacb0\uc815\uc5d0\uc11c \uc790\uc720\ub85c\uc6cc\uc9c0\uba70, \uc2dc\uc7a5 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ubc1c\uacac\ud558\uace0 \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uac70\ub798 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
1.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h3>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uc5ec \ud328\ud134\uc744 \uc54c\uc544\ub0b4\uace0 \uc608\uce21 \ud560 \uc218 \uc788\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \ud06c\uac8c \uc138 \uac00\uc9c0 \uc720\ud615\uc73c\ub85c \ubd84\ub958\ub429\ub2c8\ub2e4:<\/p>\n
\n
\uc9c0\ub3c4 \ud559\uc2b5(Supervised Learning)<\/strong> – \ub808\uc774\ube14\uc774 \uc788\ub294 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uc5ec, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n
\ube44\uc9c0\ub3c4 \ud559\uc2b5(Unsupervised Learning)<\/strong> – \ub808\uc774\ube14\uc774 \uc5c6\ub294 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uc5ec \ub370\uc774\ud130\uc758 \uc228\uaca8\uc9c4 \uad6c\uc870\ub97c \ubc1c\uacac\ud569\ub2c8\ub2e4.<\/li>\n
\uac15\ud654 \ud559\uc2b5(Reinforcement Learning)<\/strong> – \uc8fc\uc5b4\uc9c4 \ud658\uacbd\uc5d0\uc11c \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \uacb0\uc815\ud558\uae30 \uc704\ud574 \ubcf4\uc0c1\uc744 \ucd5c\ub300\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
1.2 \ub525\ub7ec\ub2dd\uc758 \uc811\uadfc<\/h3>\n
\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc758 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0, \uc74c\uc131, \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0\uc11c \ub192\uc740 \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \uce35\uc744 \uc313\uc544 \uc810\uc810 \ub354 \ubcf5\uc7a1\ud55c \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 PLSA \uad6c\ud604\uc5d0 \ucd08\uc810\uc744 \ub9de\ucd94\uc9c0\ub9cc, \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \uae30\ubc95\ub3c4 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\ub2e4\ub294 \uc810\uc744 \uc5b8\uae09\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2. PLSA(Probabilistic Latent Semantic Analysis)\ub780?<\/h2>\n
PLSA\ub294 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130(\ubb38\uc11c-\ub2e8\uc5b4 \ud589\ub82c)\ub85c\ubd80\ud130 \uc228\uaca8\uc9c4 \uc8fc\uc81c\ub97c \ucc3e\uace0 \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud1b5\uacc4\uc801 \uae30\ubc95\uc785\ub2c8\ub2e4. PLSA\ub294 \ubb38\uc11c\uc640 \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\uc5d0\uc11c \uc7a0\uc7ac\uc801\uc778 \uc8fc\uc81c\ub97c \ucd94\ucd9c\ud568\uc73c\ub85c\uc368, \uae08\uc735 \ub370\uc774\ud130\uc5d0\uc11c\ub3c4 \ube44\uc2b7\ud55c \ubd84\uc11d\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/p>\n
2.1 PLSA\uc758 \uae30\ubcf8 \uc6d0\ub9ac<\/h3>\n
PLSA\ub294 \ubaa8\ub378\uc774 \uc81c\uc2dc\ud558\ub294 \ubd84\ud3ec\ub97c \uae30\ubc18\uc73c\ub85c, \uac01 \ubb38\uc11c\uac00 \ud2b9\uc815 \uc8fc\uc81c\uc5d0 \ub300\ud574 \uc5bc\ub9c8\ub098 \uc798 \uc124\uba85\ub418\ub294\uc9c0\ub97c \ud655\ub960\uc801\uc73c\ub85c \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc774 \uc811\uadfc \ubc29\uc2dd\uc740 \ubb38\uc11c\uc5d0 \ud3ec\ud568\ub41c \ub2e8\uc5b4\uc758 \ube48\ub3c4\uc218\ub97c \uace0\ub824\ud558\uc5ec \uc8fc\uc81c-\ub2e8\uc5b4 \ubd84\ud3ec\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud2b9\uc815 \uc8fc\uc81c(\uc608: \ub9c8\ucf13 \ud2b8\ub80c\ub4dc)\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. PLSA\uc758 \uc218\ud559\uc801 \ubaa8\ub378\uc740 \ubcf4\ud1b5 EM(Expectation-Maximization) \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
3. sklearn\uc744 \uc774\uc6a9\ud55c PLSA \uad6c\ud604<\/h2>\n
\uc774\uc81c \uc2e4\uc81c\ub85c Python\uc758 scikit-learn<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec PLSA\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ub2e8\uacc4\ub97c \ud1b5\ud574 \ub370\uc774\ud130 \uc900\ube44, \ubaa8\ub378 \ud6c8\ub828 \ubc0f \ud3c9\uac00\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
3.1 \ud544\uc694 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n
pip install scikit-learn numpy pandas<\/code><\/pre>\n3.2 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n\uc6b0\uc120, \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uae08\uc735 \ub370\uc774\ud130\ub294 \ubcf4\ud1b5 CSV \ud615\uc2dd\uc73c\ub85c \uc81c\uacf5\ub418\ubbc0\ub85c pandas\ub97c \uc774\uc6a9\ud574 \uc774\ub97c \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n import pandas as pd\n\n# CSV \ud30c\uc77c\uc5d0\uc11c \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('financial_data.csv')\nprint(data.head())<\/code><\/pre>\n3.3 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n\ub370\uc774\ud130\uc758 \uacb0\uce21\uce58, \uc774\uc0c1\uce58 \ub4f1\uc744 \ucc98\ub9ac\ud558\uc5ec \ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ub370\uc774\ud130\ub97c \uc815\uc81c\ud569\ub2c8\ub2e4. \ub610\ud55c, \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\ub2e4\uba74 \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac\ub3c4 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n # \uacb0\uce21\uce58 \ucc98\ub9ac\ndata.dropna(inplace=True)\n\n# \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac \uc608\uc2dc\ndata['text'] = data['text'].str.lower() # \uc18c\ubb38\uc790 \ubcc0\ud658\n...<\/code><\/pre>\n3.4 PLSA \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud6c8\ub828<\/h3>\n\uc774\uc81c PLSA \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uace0 \uc774 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \ud6c8\ub828\ud560 \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n from sklearn.decomposition import NMF\n\n# NMF\ub97c \uc774\uc6a9\ud55c PLSA \uad6c\ud604\nnum_topics = 5 # \uc8fc\uc81c \uc218\nmodel = NMF(n_components=num_topics)\nW = model.fit_transform(data_matrix) # \ubb38\uc11c-\uc8fc\uc81c \ud589\ub82c\nH = model.components_ # \uc8fc\uc81c-\ub2e8\uc5b4 \ud589\ub82c<\/code><\/pre>\n3.5 \uacb0\uacfc \ubd84\uc11d<\/h3>\n\ubaa8\ub378\uc774 \ud559\uc2b5\ud55c \uc8fc\uc81c\ub97c \ud655\uc778\ud558\uae30 \uc704\ud574, \uac01 \uc8fc\uc81c\uc5d0 \ub300\ud574 \uac00\uc7a5 \uac00\uc911\uce58\uac00 \ub192\uc740 \ub2e8\uc5b4\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4.<\/p>\n for topic_idx, topic in enumerate(H):\n print(\"Topic #%d:\" % topic_idx)\n print(\" \".join([str(i) for i in topic.argsort()[:-10 - 1:-1]))<\/code><\/pre>\n4. PLSA\uc758 \ucd9c\ubc1c\uc810\uacfc \uc801\uc6a9 \ubd84\uc57c<\/h2>\nPLSA\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \uadf8 \uc720\uc6a9\uc131\uc744 \uc785\uc99d\ud574\uc654\uc73c\uba70, \ub2e4\uc591\ud55c \uae08\uc735 \uc2dc\uc7a5\uc758 \ud328\ud134 \ubd84\uc11d\uc5d0\ub3c4 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uc8fc\uc2dd\uc758 \ub274\uc2a4 \ud5e4\ub4dc\ub77c\uc778\uc744 \uc8fc\uc81c\ubcc4\ub85c \ubd84\ub958\ud568\uc73c\ub85c\uc368 \ud574\ub2f9 \uc8fc\uc2dd\uc758 \uc2dc\uc7a5 \ubc18\uc751\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n 4.1 PLSA\uc758 \uc7a5\uc810<\/h3>\n\n\uc7a0\uc7ac \uc8fc\uc81c\ub97c \ud1b5\ud55c \ub370\uc774\ud130 \uac04 \uc228\uaca8\uc9c4 \uad00\uacc4 \ubc1c\uacac<\/li>\n \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uc758\ubbf8 \uc788\ub294 \ud328\ud134 \ud30c\uc545<\/li>\n \uc758\uc0ac \uacb0\uc815\uc5d0 \ud544\uc694\ud55c \uc815\ubcf4 \uc81c\uacf5<\/li>\n<\/ul>\n4.2 \ud55c\uacc4\uc640 \uc8fc\uc758 \uc0ac\ud56d<\/h3>\n\n\ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uc774 \uc99d\uac00\ud560 \uacbd\uc6b0 \uacfc\uc801\ud569 \uac00\ub2a5\uc131 \uc874\uc7ac<\/li>\n \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc774 \uacb0\uacfc\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce68<\/li>\n<\/ul>\n5. \uacb0\ub860<\/h2>\n\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc810\uc810 \ub354 \uc911\uc694\ud574\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 PLSA\uc640 \uac19\uc740 \uae30\ubc95\uc740 \ub370\uc774\ud130\uc5d0\uc11c \uc720\uc758\ubbf8\ud55c \ud328\ud134\uc744 \ubc1c\uacac\ud558\uace0 \uc2dc\uc7a5 \ud589\ub3d9\uc744 \uc608\uce21\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c \uc18c\uac1c\ud55c scikit-learn\uc744 \ud65c\uc6a9\ud55c PLSA \uad6c\ud604 \ubc29\ubc95\uc774 \uc5ec\ub7ec\ubd84\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n 6. \ucd94\uac00 \ud559\uc2b5 \uc790\ub8cc<\/h2>\n\nscikit-learn NMF Documentation<\/a><\/li>\n Understanding PLSA in Python<\/a><\/li>\n Kaggle Datasets<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\ucd5c\uadfc \uba87 \ub144 \uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc778\uae30\ub294 \uae09\uc99d\ud588\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub370\uc774\ud130 \uacfc\ud559 \ubc0f \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uadf8 \uc911\uc2ec\uc5d0 \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \uc774\ub97c \uc774\uc6a9\ud55c \ud000\ud2b8 \ub9e4\ub9e4\uc758 \uc774\uc810\uc744 \ub17c\uc758\ud558\uba70, \ub9c8\uc9c0\ub9c9\uc73c\ub85c scikit-learn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud55c PLSA(Probabilistic Latent Semantic Analysis)\uc758 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uacfc \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c pLSA\uc758 \uad6c\ud604 \ubc29\ubc95”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-28744","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c pLSA\uc758 \uad6c\ud604 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/28744\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c pLSA\uc758 \uad6c\ud604 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ucd5c\uadfc \uba87 \ub144 \uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc778\uae30\ub294 \uae09\uc99d\ud588\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub370\uc774\ud130 \uacfc\ud559 \ubc0f \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uadf8 \uc911\uc2ec\uc5d0 \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \uc774\ub97c \uc774\uc6a9\ud55c \ud000\ud2b8 \ub9e4\ub9e4\uc758 \uc774\uc810\uc744 \ub17c\uc758\ud558\uba70, \ub9c8\uc9c0\ub9c9\uc73c\ub85c scikit-learn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud55c PLSA(Probabilistic Latent Semantic Analysis)\uc758 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uacfc \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 … \ub354 \ubcf4\uae30 "\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c pLSA\uc758 \uad6c\ud604 \ubc29\ubc95"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/28744\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-27T16:14:51+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:58:14+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" 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