[B! dataset] ttakezawaã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

ttakezawa id:ttakezawa

datasetã«é–¢ã™ã‚‹ttakezawaã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (24)

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ã€Žæ—¥æœ¬èªžè©±ã—è¨€è‘‰ã‚³ãƒ¼ãƒ‘ã‚¹ã€ã¯ã€æ—¥æœ¬èªžã®è‡ªç™ºéŸ³å£°ã‚’å¤§é‡ã«ã‚ã¤ã‚ã¦å¤šãã®ç ”ç©¶ç”¨æƒ…å ±ã‚’ä»˜åŠ ã—ãŸè©±ã—è¨€è‘‰ç ”ç©¶ç”¨ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã§ã‚ã‚Šã€å›½ç«‹å›½èªžç ”ç©¶æ‰€ãƒ» æƒ…å ±é€šä¿¡ç ”ç©¶æ©Ÿæ§‹ï¼ˆæ—§é€šä¿¡ç·åˆç ”ç©¶æ‰€ï¼‰ãƒ» æ±äº¬å·¥æ¥å¤§å¦ ãŒå…±åŒé–‹ç™ºã—ãŸã€è³ªãƒ»é‡ã¨ã‚‚ã«ä¸–ç•Œæœ€é«˜æ°´æº–ã®è©±ã—è¨€è‘‰ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã§ã™ã€‚æœ¬ã‚³ãƒ¼ãƒ‘ã‚¹ã¯éŸ³å£°è¨€èªžæƒ…å ±å‡¦ç†ã€è‡ªç„¶è¨€èªžå‡¦ç†ã€ æ—¥æœ¬èªžå¦ã€è¨€èªžå¦ã€éŸ³å£°å¦ã€å¿ƒç†å¦ã€ç¤¾ä¼šå¦ã€æ—¥æœ¬èªžæ•™è‚²ã€è¾žæ›¸ç·¨çº‚ãªã©å¹…åºƒã„é ˜åŸŸã§åˆ©ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ 2011.11.28ã€Žæ—¥æœ¬èªžè©±ã—è¨€è‘‰ã‚³ãƒ¼ãƒ‘ã‚¹ã€ç¬¬ï¼“åˆ·ã®å—ä»˜ã‚’é–‹å§‹ã—ã¾ã—ãŸã€‚(æ›´æ–°ï¼š2011/11/28) 2011.02.18åœ¨åº«ãŒãªããªã‚Šã¾ã—ãŸã®ã§ã€ä¸€æ—¦å—ä»˜ã‚’ä¸æ¢ã—ã¾ã™ã€‚ï¼ˆæ›´æ–°ï¼š2011/2/18ï¼‰ 2008.05.02ã€Žæ—¥æœ¬èªžè©±ã—è¨€è‘‰ã‚³ãƒ¼ãƒ‘ã‚¹ã€ç¬¬ï¼’åˆ·ã®å—ä»˜ã‚’é–‹å§‹ã—ã¾ã—ãŸ(æ›´æ–°ï¼š2008/05/02) 2008.03.24å ±å‘Šæ›¸ã€Žæ—¥æœ¬èªžè©±ã—è¨€è‘‰ã‚³ãƒ¼ãƒ‘ã‚¹ã®æ§‹ç¯‰æ³•ã€ãŒãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã§ãã‚‹ã‚ˆ
ttakezawa 2011/11/29
nlp

dataset

corpus
ãƒªãƒ³ã‚¯
å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã‚’ç„¡æ–™ã§æ‰‹ã«å…¥ã‚Œã‚‹ã“ã¨ã®ã§ãã‚‹ã‚µã‚¤ãƒˆã¾ã¨ã‚ - nokunoã®æ—¥è¨˜
å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ãŒå…¬é–‹ã•ã‚Œã¦ã„ã‚‹ã‚µã‚¤ãƒˆã«ã¤ã„ã¦ä»¥ä¸‹ã®Quoraã§id:makimotoã•ã‚“ãŒè³ªå•ã—ã¦ã„ã¾ã—ãŸã€‚Data: Where can I get large datasets open to the public? - Quoraä»¥ä¸‹ã€ç´¹ä»‹ã•ã‚Œã¦ã„ã‚‹ã‚µã‚¤ãƒˆã®ä¸€è¦§ã§ã™ã€‚ä¸€éƒ¨æœ‰æ–™ã®ã‚‚ã®ã‚‚ã‚ã‚‹ã‚ˆã†ã§ã™ã€‚UCI Machine Learning RepositoryPublic Data Sets : Amazon Web ServicesCRAWDADno titleCity of Chicago | Data PortalGovLoop | Social Data Network for Governmentdata.gov.uk | Opening up governmentData.Medicare.GovData.Seattle.Gov | Seattleâ€™s Data SiteOp
ttakezawa 2011/06/16
quora

dataset

webservice
ãƒªãƒ³ã‚¯
yatsãŒå›žåŽã—ãŸTwitteræ—¥æœ¬èªžåœ 9æœˆ(1/2) - ä¸å¯è¦–ç‚¹
ä¹…ã—ã¶ã‚Šã«Twitteræ—¥æœ¬èªžåœã®ãƒ€ãƒ³ãƒ—ã‚’å…¬é–‹ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ 9/1-9/16ã®1.77å„„ã¤ã¶ã‚„ãã®MySQLãƒ€ãƒ³ãƒ—ã§ã™(load dataã§å–ã‚Šè¾¼ã‚€ã‚¿ã‚¤ãƒ—) yatsã®åŽé›†å¯¾è±¡ã¯ å…¬é–‹ãƒ¦ãƒ¼ã‚¶ãƒ¼çŠ¶æ…‹ã§ã¤ã¶ã‚„ã‹ã‚ŒãŸã‚‚ã®ã®ã†ã¡ éŽåŽ»3é€±é–“ä»¥å†…ã«ã¤ã¶ã‚„ã„ãŸãƒ¦ãƒ¼ã‚¶ãƒ¼ã‹ã‚‰ã®ã‚‚ã®ã€ ç´¯ç©200ã€œ400ã¤ã¶ã‚„ãã®æ—¥æœ¬èªžãƒ¦ãƒ¼ã‚¶ãƒ¼ã‹ã‚‰ã®ã‚‚ã® ã§ã™ã€‚ãƒ™ã‚¹ãƒˆã‚¨ãƒ•ã‚©ãƒ¼ãƒˆã§ã™ã€‚ streaming apiã§æµã‚Œã¦ãã‚‹ã¤ã¶ã‚„ãã‚‚ã ã„ãŸã„è¨˜éŒ²ã—ã¦ã„ã¾ã™ã€‚ ã‚¹ã‚ãƒ¼ãƒžï¼š CREATE TABLE `buffer_20100916` ( `id_autoinc` bigint(20) unsigned NOT NULL AUTO_INCREMENT, `id` bigint(20) unsigned NOT NULL, `user` varchar(20) NOT NULL, `content` text NOT NUL
ttakezawa 2010/09/29
twitter

dataset
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Info Chimps
Cloud services for big data applications is certainly something that brings a number of impressive benefits to the table. Choosing the right cloud services for your specific big data applications can be challenging, particularly if you are just beginning to work with big data applications, or if you are only now starting to explore the full potential offered through cloud services. To be sure, cho
ttakezawa 2010/07/21
twitter

dataset
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CURRENT LAB: Social Networking Projects
#Research on Facebook, Twitter, and other OSNs Welcome. My group has been studying online social networks with a primary focus on Facebook and Twitter.Â Facebook is currently the largest online social network, with more than 360 million active users worldwide (February 2010).Â It is also an interesting and vibrant application platform for everything from shared Netflix reviews to Hugg and social
ttakezawa 2010/05/21
research

facebook

dataset

web
ãƒªãƒ³ã‚¯
YouTube Dataset
1. Datasets of Normal Crawl We consider all the YouTube videos to form a directed graph, where each video is a node in the graph. If a video b is in the related video list (first 20 only) of a video a, then there is a directed edge from a to b. Our crawler uses a breadth-first search to find videos in the graph. We define the initial set of 0-depth video IDs, which the crawler reads in to a queue
ttakezawa 2010/05/14
youtube

dataset

graph
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Tastes, Ties, and Time: Facebook data release | Berkman Klein Center
In collaboration with Harvard sociology graduate students Kevin Lewis and Marco Gonzalez, and with UCLA professor Andreas Wimmer and Harvard professor Nicholas Christakis, Berkman Fellow Jason Kaufman has made available a first wave of Facebook.com data through the Dataverse Network Project. The dataset comprises machine-readable files of virtually all the information posted on approximately 1,700
ttakezawa 2010/05/13
facebook

dataset

research
ãƒªãƒ³ã‚¯
SNAP: Stanford Network Analysis Project
We are inviting applications for postdoctoral positions in Foundation Models for Biomedicine. We have open positions for undergraduate and graduate research assistants. The application form and project descriptions can be found here. SNAP for C++: Stanford Network Analysis Platform Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library. It is writt
ttakezawa 2010/05/13
research

dataset

network

nlp

web

graph
ãƒªãƒ³ã‚¯
What is Twitter, a Social Network or a News Media? - WWW'10
ANLAB Traces What is Twitter, a Social Network or a News Media? (WWW2010) Mining communities in networks: a solution for consistency and its evaluation (IMC2009) Consistent Community Identification in Very Large Networks (MICNET2009) Evaluation of VoIP Quality over WiBro (PAM2008) I Tube, You Tube, Everybody Tubes (IMC2007) Analysis of Topological Characteristics of Huge Online Social Networking S
ttakezawa 2010/04/23
web

paper

research

twitter

dataset

graph
ãƒªãƒ³ã‚¯
Freebase
Send feedback Data Dumps Stay organized with collections Save and categorize content based on your preferences. Data Dumps are a downloadable version of the data in Freebase. They constitute a snapshot of the data stored in Freebase and the Schema that structures it, and are provided under the same CC-BY license. The Freebase/Wikidata mappings are provided under the CC0 license. Freebase Triples F
ttakezawa 2010/01/12
webservice

database

dataset
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Enron Email Dataset
This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. The em ai
ttakezawa 2010/01/05
dataset

research

nlp
ãƒªãƒ³ã‚¯
Thesaurus.com - find synonyms and antonyms
TodayHoroscopes Words That Every Aries Should KnowAries is the perfect sign to usher in spring, a natural time of rebirth, change, and growth. Read up on Aries traits here. Read more.
ttakezawa 2009/12/24
dataset

dictionary

english

webservice
ãƒªãƒ³ã‚¯
ATR - DNI â‰« è¦–è¦šåƒå†æ§‹æˆfMRIãƒ‡ãƒ¼ã‚¿
ã“ã®ãƒšãƒ¼ã‚¸ã§ã¯ã€è¦–è¦šåƒå†æ§‹æˆã®ç ”ç©¶ã§ç”¨ã„ã‚‰ã‚ŒãŸMATLABã‚³ãƒ¼ãƒ‰ã¨fMRIãƒ‡ãƒ¼ã‚¿ã‚’ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ æ¦‚è¦ è¦–è¦šåƒå†æ§‹æˆã®ç ”ç©¶Miyawaki Y et al. (2008): Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron. Dec 10;60(5):915-29.ã§ç”¨ã„ãŸfMRIãƒ‡ãƒ¼ã‚¿ã‚’å…¬é–‹ã—ã¦ãŠã‚Šã¾ã™ã®ã§ã€ã©ãªãŸã§ã‚‚ã”è‡ªç”±ã«ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã—ã¦ãã ã•ã„ã€‚ ç§ãŸã¡ã®è«–æ–‡ä¸ã§ã¯ã€â€œsparse multinomial logistic regressionâ€ã¨ å‘¼ã‚“ã§ãŠã‚Šã¾ã™ã‚«ã‚¹ã‚¿ãƒ ãƒ¡ã‚¤ãƒ‰ã®æ©Ÿæ¢°å¦ç¿’ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ç”¨ã„ã¦ç©ºé–“çš„ã«å¤šé‡è§£åƒåº¦ã‚’æŒã¤ãƒ‡ã‚³ãƒ¼ãƒ€ã‚’å¦ç¿’ã•ã›ã€ãã‚Œã‚‰ã‚’ç°¡å˜ãªç·šå½¢ãƒ¢ãƒ‡ãƒ«ã«åŸºã¥ã„ã¦
ttakezawa 2009/12/04
Sparse Logistic Regression

machinelearning

dataset

matlab

image processing
ãƒªãƒ³ã‚¯
Resources for NLP
è‡ªç„¶è¨€èªžå‡¦ç†ã®ãŸã‚ã®ãƒªã‚½ãƒ¼ã‚¹ æ—¥æœ¬èªžå½¢æ…‹ç´ è§£æžã‚·ã‚¹ãƒ†ãƒ JUMAN æ—¥æœ¬èªžæ§‹æ–‡è§£æžã‚·ã‚¹ãƒ†ãƒ KNP äº¬éƒ½å¤§å¦ãƒ†ã‚ã‚¹ãƒˆã‚³ãƒ¼ãƒ‘ã‚¹ Webã‹ã‚‰è‡ªå‹•æ§‹ç¯‰ã—ãŸå¤§è¦æ¨¡æ ¼ãƒ•ãƒ¬ãƒ¼ãƒ è§£æžçµæžœæ±Žç”¨è¡¨ç¤ºãƒ„ãƒ¼ãƒ« TableDisplay ã“ã‚Œã‚‰ã®ãƒªã‚½ãƒ¼ã‚¹ã«é–¢ã™ã‚‹å¾¡æ„è¦‹, å¾¡è³ªå•ã¯ nl-resource ã‚ã£ã¨ nlp.kuee.kyoto-u.ac.jp å®›ã«ãŠé¡˜ã„è‡´ã—ã¾ã™ã€‚
ttakezawa 2009/11/28
nlp

dataset

corpus
ãƒªãƒ³ã‚¯
äº¬éƒ½ãƒ†ã‚ã‚¹ãƒˆã‚³ãƒ¼ãƒ‘ã‚¹
äº¬éƒ½ãƒ†ã‚ã‚¹ãƒˆã‚³ãƒ¼ãƒ‘ã‚¹ Version 4.0 æ¯Žæ—¥æ–°èžã®è¨˜äº‹ã«å„ç¨®è¨€èªžæƒ…å ±ã‚’äººæ‰‹ã§ä»˜ä¸Žã—ãŸãƒ†ã‚ã‚¹ãƒˆã‚³ãƒ¼ãƒ‘ã‚¹ã§ã™ã€‚95å¹´1æœˆ1æ—¥ã‹ã‚‰17æ—¥ã¾ã§ã®å…¨è¨˜äº‹ã€ç´„2ä¸‡æ–‡ã€1æœˆã‹ã‚‰12æœˆã¾ã§ã®ç¤¾èª¬è¨˜äº‹ã€ç´„2ä¸‡æ–‡ã€è¨ˆç´„4ä¸‡æ–‡ã«å¯¾ã—ã¦ã€å½¢æ…‹ç´ ãƒ»æ§‹æ–‡æƒ…å ±ã‚’ä»˜ä¸Žã—ã¦ã„ã¾ã™ã€‚ã“ã‚Œã‚‰ã®æƒ…å ±ã¯ã€å½¢æ…‹ç´ è§£æžã‚·ã‚¹ãƒ†ãƒ JUMANã€æ§‹æ–‡è§£æžã‚·ã‚¹ãƒ†ãƒ KNPã§è‡ªå‹•è§£æžã‚’è¡Œã„ã€ãã®çµæžœã‚’äººæ‰‹ã§ä¿®æ£ã—ãŸã‚‚ã®ã§ã™ã€‚ ã•ã‚‰ã«ã€ã“ã®ã†ã¡ã®5,000æ–‡ã«å¯¾ã—ã¦ã¯ã€æ ¼é–¢ä¿‚ã€ç…§å¿œãƒ»çœç•¥é–¢ä¿‚ã€å…±å‚ç…§ã®æƒ…å ±ã‚’ä»˜ä¸Žã—ã¦ã„ã¾ã™ã€‚ ã‚³ãƒ¼ãƒ‘ã‚¹ã‚’ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ (7,990,765 bytes) å½¢æ…‹ç´ ãƒ»æ§‹æ–‡æƒ…å ±ã®ã‚¿ã‚°ä»˜ã‘åŸºæº–ãƒžãƒ‹ãƒ¥ã‚¢ãƒ« æ ¼é–¢ä¿‚ã€ç…§å¿œãƒ»çœç•¥é–¢ä¿‚ã€å…±å‚ç…§æƒ…å ±ã®ã‚¿ã‚°ä»˜ã‘åŸºæº–ãƒžãƒ‹ãƒ¥ã‚¢ãƒ« â€» ã“ã‚Œã‚‰ã®ãƒžãƒ‹ãƒ¥ã‚¢ãƒ«ã¯ã‚³ãƒ¼ãƒ‘ã‚¹ã®ãƒ‘ãƒƒã‚±ãƒ¼ã‚¸ã«ã‚‚åŒæ¢±ã•ã‚Œã¦ã„ã¾ã™ã€‚ æ³¨æ„ç‚¹ï¼šã“ã“ã«å«ã¾ã‚Œã‚‹ã®ã¯å½¢æ…‹ç´ ãƒ»æ§‹æ–‡ãƒ»é–¢ä¿‚ã®ä»˜åŠ æƒ…å ±ã ã‘ã§, ã‚‚ã¨ã®æ¯Žæ—¥æ–°èžãƒ‡ãƒ¼ã‚¿ã¯å«ã¾ã‚Œ
ttakezawa 2009/10/30
dataset

nlp

machinelearning
ãƒªãƒ³ã‚¯
å½¢æ…‹ç´ è§£æžè¾žæ›¸ UniDic
äººæ°—ãƒ‡ãƒ™ãƒãƒƒãƒ‘ãƒ¼ Playâ€™N Go ã¯ã€2017å¹´ã«ãƒ¦ãƒ‹ãƒ¼ã‚¯ãªã‚¹ãƒãƒƒãƒˆã‚’ç™ºå£²ã—ã¾ã—ãŸ ãƒ ãƒ¼ãƒ³ãƒ—ãƒªãƒ³ã‚»ã‚¹. ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ ã‚¹ãƒãƒƒãƒˆã‚²ãƒ¼ãƒ ãƒ ãƒ¼ãƒ³ãƒ—ãƒªãƒ³ã‚»ã‚¹ ã“ã®ã‚²ãƒ¼ãƒ ã¯ã€ãã®ãƒ¦ãƒ‹ãƒ¼ã‚¯ãªã‚ªãƒ—ã‚·ãƒ§ãƒ³ã€ã‚·ãƒ³ãƒ—ãƒ«ãªæ“ä½œæ€§ã€ã‚¢ãƒ‹ãƒ¡ã‚’ãƒ¢ãƒãƒ¼ãƒ•ã«ã—ãŸã‚«ãƒ©ãƒ•ãƒ«ãªã‚°ãƒ©ãƒ•ã‚£ãƒƒã‚¯ã«ã‚ˆã‚Šã€çž¬ãé–“ã«ãƒ—ãƒ¬ã‚¤ãƒ¤ãƒ¼ã‹ã‚‰äººæ°—ã‚’é›†ã‚ã¾ã—ãŸã€‚ ã‚¹ãƒãƒƒãƒˆãƒžã‚·ãƒ³ã® ãƒ ãƒ¼ãƒ³ãƒ—ãƒªãƒ³ã‚»ã‚¹ ã¯ã€æ¼«ç”»ã€Žç¾Žå°‘å¥³æˆ¦å£«ã‚»ãƒ¼ãƒ©ãƒ¼ãƒ ãƒ¼ãƒ³ã€ã®ãƒ†ãƒ¼ãƒžã‚’æ˜Žã‚‰ã‹ã«ã™ã‚‹ã€‚ç¬¬1å·»ã¯1992å¹´ã«ç™ºå£²ã•ã‚Œã€ç¾åœ¨ã‚‚äººæ°—ãŒã‚ã‚Šã¾ã™ã€‚ã‚’å¯èƒ½ã«ã—ã¾ã™ MoonPrincess ã¯ã€é•·å¹´ã«ã‚ãŸã‚Šæœ€é«˜ã®ã‚¹ãƒãƒƒãƒˆã®ãƒªã‚¹ãƒˆã‚’ãƒªãƒ¼ãƒ‰ã—ã€ãƒ—ãƒ¬ã‚¤ãƒ¤ãƒ¼ã«å¯›å¤§ãªé…å½“ã ã‘ã‚’æä¾›ã—ã€ã‚²ãƒ¼ãƒ ãƒ—ãƒ¬ã‚¤ã¸ã®é–¢å¿ƒã‚’ä¿è¨¼ã—ã¾ã™ã€‚ ã™ã¹ã¦ã®ã‚·ãƒ³ãƒœãƒ«ãŒé›°å›²æ°—ã‚’å¼·èª¿ã™ã‚‹ ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ ã‚¹ãƒãƒƒãƒˆã‚²ãƒ¼ãƒ ãƒ ãƒ¼ãƒ³ãƒ—ãƒªãƒ³ã‚»ã‚¹, å¿ƒåœ°ã‚ˆã„ã‚µã‚¦ãƒ³ãƒ‰ãƒˆãƒ©ãƒƒã‚¯ãŒã‚²ãƒ¼ãƒ ãƒ—ãƒ¬ã‚¤ã‚’å¼•ãç«‹ã¦ã€éŸ³æ¥½ã‚’ãƒ‘ãƒ¼ã‚½ãƒŠãƒ©ã‚¤ã‚ºã§ãã‚‹ãŸã‚ã€ãƒ¦ãƒ¼ã‚¶ãƒ¼ã¯ã‚¹ãƒãƒƒ
ttakezawa 2009/10/28
nlp

dataset
ãƒªãƒ³ã‚¯
Wikipedia:ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ - Wikipedia
ã‚¦ã‚£ã‚ãƒšãƒ‡ã‚£ã‚¢ã®ã‚³ãƒ³ãƒ†ãƒ³ãƒ„ãªã©ã®ãƒ‡ãƒ¼ã‚¿ã¯ã€å†é…å¸ƒã‚„å†åˆ©ç”¨ã®ãŸã‚ã«åˆ©ç”¨ã§ãã‚‹ä¸€å…ƒåŒ–ã•ã‚ŒãŸãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ãƒ»ãƒ€ãƒ³ãƒ—ã§ã®æä¾›ãŒè¡Œã‚ã‚Œã¦ã„ã¾ã™ã€‚ã‚¯ãƒãƒ¼ãƒ©ã‚’ç”¨ã„ã¦ã‚³ãƒ³ãƒ†ãƒ³ãƒ„ã‚’åŽé›†ã—ãªã„ã§ãã ã•ã„ã€‚ã“ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ãƒ»ãƒ€ãƒ³ãƒ—ã®ç”Ÿæˆã¯ä¸å®šæœŸã«è¡Œã‚ã‚Œã¦ã„ã¾ã™ã€‚ ã‚¦ã‚£ã‚ãƒšãƒ‡ã‚£ã‚¢ã®ã‚³ãƒ³ãƒ†ãƒ³ãƒ„ã¯ Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) ãŠã‚ˆã³ GNU Free Documentation License (GFDL) ã®ä¸‹ã«ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã•ã‚Œã¦ã„ã¾ã™ï¼ˆWikipedia:è‘—ä½œæ¨©ã¨åˆ©ç”¨è¦ç´„ã‚’å‚ç…§ï¼‰ã€‚ç”»åƒãªã©ã®ãƒ¡ãƒ‡ã‚£ã‚¢ãƒ•ã‚¡ã‚¤ãƒ«ã¯ç•°ãªã‚‹ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã§æä¾›ã•ã‚Œã‚‹ã“ã¨ã‚‚ã‚ã‚Šã€ãƒ•ã‚¡ã‚¤ãƒ«ãƒšãƒ¼ã‚¸ã§æ˜Žè¨˜ã•ã‚Œã¦ã„ã¾ã™ã€‚ ã‚ˆã‚Šè©³ã—ã„è§£èª¬ã¯meta:Data dumpsï¼ˆè‹±èªžï¼‰ã‚’å‚ç…§ã—ã¦ãã ã•ã„ã€‚ å…¨ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã®ãƒ€ãƒ³ãƒ—ï¼šdu
ttakezawa 2009/10/28
wikipedia

dataset
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ttakezawa 2009/10/27
nlp

dataset
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Laboratory for Web Algorithmics
We provide large graphs that can be accessed using WebGraph. Please read the tutorial to learn how to use such format. If you publish results based on these graphs, please acknowledge the usage of WebGraph by quoting the following paper: @inproceedings{BoVWFI, author ="Paolo Boldi and Sebastiano Vigna", title = "The {W}eb{G}raph Framework {I}: {C}ompression Techniques", year = 2004, booktitle="Pro
ttakezawa 2009/10/02
graph

dataset

web

paper
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The Homepage of Nearest Neighbors and Similarity Search
The Homepage of Nearest Neighbors and Similarity Search Maintained by Yury Lifshits Update: this page is frozen. Please visit the successor page by Arnoldo Muller To preprocess a database of N objects so that given a query object, one can effectively determine its nearest neighbors in database The purpose of this page is to collect links, people, ideas, keywords, papers, slides, code and data sets
ttakezawa 2009/10/02
algorithm

ir

search

recommendation

dataset

tutorial
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