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statisticsã«é–¢ã™ã‚‹takadoã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (105)

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takado 2010/09/14
academic

presentation

statistics

math
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Active Learning with Statistical Models
takado 2010/03/19
active

learning

statistics

tutorial

machinelearning
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Confidence-Weighted Linear Classification
We introduce confidence-weighted linear classifiers, a new class of algorithms that maintain confidence information about classifier parameters. Learning in this framework updates parameters by estima Read more
takado 2010/03/15
machinelearning

presentation

statistics
ãƒªãƒ³ã‚¯
Tutorials on Topics in Statistical Pattern Recognition
General Glossary Overview Specific Methods Distance Metrics Bayesian Methods Sequential Classifiers Discriminating Feature Methods Fuzzy K-Nearest Neighbor Linear and Higer Order Discriminant Functions Minimum Distance Multi-classifier fusion Neural Network and Perceptron Methods Markov Random Field Genetic Algorithm Nonparametric Estimation Support Vector Machine Expectation Maximization Etc. App
takado 2010/03/15
machinelearning

statistics
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Data Commons
Data Commons aggregates and harmonizes global, open data, giving everyone the power to uncover insights with natural language questions
takado 2010/03/09
Data visualizations for a changing world

google

visualization

datamining

graphics

research

statistics
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CSE 515 - Statistical Methods in Computer Science - Spring 2009
takado 2010/01/29
machinelearning

statistics
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ç¢ºçŽ‡ã‚²ãƒ¼ãƒ ã®è©±
takado 2010/01/21
ãƒ‘ãƒãƒ³ãƒ‰ã®ãƒ‘ãƒ©ãƒ‰ãƒƒã‚¯ã‚¹ï¼Žã€Œè² ã‘ç¶šã‘ã‚‹äºŒã¤ã®ã‚²ãƒ¼ãƒ ã‚’ã€å¾—ç‚¹ã¯å…±é€šã§ãƒ©ãƒ³ãƒ€ãƒ ã«åˆ‡ã‚Šæ›¿ãˆãªãŒã‚‰è©¦è¡Œã‚’é‡ãã‚‹ã¨ã€ãªã‚“ã¨å®‰å®šçš„ã«å¾—ç‚¹ãŒå¢—ãˆã‚‹ã‚²ãƒ¼ãƒ ã«ãªã‚‹ã€

math

statistics
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254A, Notes 0: A review of probability theory
254A, Notes 0: A review of probabilityÂ theory 1 January, 2010 in 254A - random matrices, math.CA, math.PR | Tags: almost sure convergence, conditioning, convergence in probability, independence, moments, random variables | by Terence Tao In preparation for my upcoming course on random matrices, I am briefly reviewing some relevant foundational aspects of probability theory, as well as setting up b
takado 2010/01/03
math

tutorial

statistics
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çµ±è¨ˆçš„ã«æ£ã—ã„ãƒ©ãƒ³ã‚ãƒ³ã‚°ã‚’è¡Œã†æ–¹æ³• - Hello, world! - s21g
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 ãƒã‚¸ãƒ†ã‚£ãƒ–ï¼ãƒã‚¬ãƒ†ã‚£ãƒ–æŠ•ç¥¨ã«ã‚ˆã‚‹æ£ã—ã„ãƒ©ãƒ³ã‚ãƒ³ã‚°æ–¹æ³•ãŒä»¥ä¸‹ã®è¨˜äº‹ã§ç´¹ä»‹ã•ã‚Œã¦ã„ã¾ã™ã€‚ How Not To Sort By Average Rating ã“ã®è¨ˆç®—æ–¹æ³•ã§ã¯ã€æŠ•ç¥¨æ•°ãŒå°‘ãªã„å ´åˆã«ã¯åˆ†æ•£ãŒå¤§ããä¸æ£ç¢ºãªè©•ä¾¡ã§ã€ æŠ•ç¥¨æ•°ãŒå¤šããªã‚‹ã«ã¤ã‚Œã¦åˆ†æ•£ãŒå°ã•ãæ£ç¢ºãªè©•ä¾¡ãŒå¾—ã‚‰ã‚Œã¦ã„ã‚‹ã¨ã„ã†äº‹ã‚’è€ƒæ…®ã—ã¦ã„ã¾ã™ã€‚ä»¥ä¸‹æ•°å¼ ã“ã‚Œã¯Scoreã®ä¿¡é ¼åŒºé–“ã‚’è¡¨ã—ã¦ã„ã¾ã™ã€‚ ã“ã®ä¿¡é ¼åŒºé–“ã®ä¸‹ç•Œã‚’ãƒ©ãƒ³ã‚ãƒ³ã‚°ã®ã‚¹ã‚³ã‚¢ã«ã™ã‚Œã°è‰¯ã„äº‹ã«ãªã‚Šã¾ã™ã€‚ ã“ã“ã§ã€ã¯ã€ ã§ã™ã€‚å…¨ä½“ã«å ã‚ã‚‹ãƒã‚¸ãƒ†ã‚£ãƒ–æŠ•ç¥¨æ•°ã®å‰²åˆã§ã™ãã€‚ ã¯æ¨™æº–æ£è¦åˆ†å¸ƒä¸Šã® ä¿¡é ¼åŒºé–“ã®æœ‰æ„ç¢ºçŽ‡ã§ã™ã€‚ ã•ã¦ã€äº”æ®µéšŽè©•ä¾¡ã«ã‚ˆã‚‹Ratingã«åŒæ§˜ã®ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã‚’é©ç”¨ã™ã‚‹å ´åˆã¯ã©ã†ã—ãŸã‚‰ã„ã„ã§ã—ã‚‡ã†ã‹
takado 2009/12/30
statistics

algorithm
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CS: Robust PCA, C-SALSA is released, KAUST Jobs
takado 2009/12/22
RobustPCA

machinelearning

statistics

algorithm
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ã€Žç¢ºçŽ‡çš„ç™ºæƒ³æ³•ã€ ãƒ™ã‚¤ã‚ºæŽ¨å®šã®ã‚¢ã‚¤ãƒ‡ã‚£ã‚¢ãŒä½•ã‚ˆã‚Šã‚ˆãã‚ã‹ã‚‹ï¼ | aki note
ã€Žç¢ºçŽ‡çš„ç™ºæƒ³æ³•ã€ ãƒ™ã‚¤ã‚ºæŽ¨å®šã®ã‚¢ã‚¤ãƒ‡ã‚£ã‚¢ãŒä½•ã‚ˆã‚Šã‚ˆãã‚ã‹ã‚‹ï¼æœ€è¿‘ã®ã‚³ãƒ¡ãƒ³ãƒˆ è£½é€ æ¥ã®æ´¾é£åŠ´åƒç¦æ¢ã«ã¤ã„ã¦ç–‘å•ã«æ€ã†ã“ã¨ ã« æ´¾é£ ã‚ˆã‚Šè£½é€ æ¥ã®æ´¾é£åŠ´åƒç¦æ¢ã«ã¤ã„ã¦ç–‘å•ã«æ€ã†ã“ã¨ ã« æ´¾é£ - çŸ¥æµè¢‹ã‚«ãƒ¬ãƒ¼ãƒ‘ãƒ³ ã‚ˆã‚ŠãŸã®ã—ãèªã‚ã‚‹ãƒ»èªã¾ã›ã‚‹æ–‡ç« ï¼š BPM Story ã« Yamamoto ã‚ˆã‚Šâ€œåŠ‡å ´ç‰ˆãƒžã‚¯ãƒã‚¹F ~ã‚¤ãƒ„ãƒ¯ãƒªãƒŽã‚¦ã‚¿ãƒ’ãƒ¡â€ è¦‹ã¦ãã¾ã—ãŸ ã« akipponn ã‚ˆã‚Šâ€œåŠ‡å ´ç‰ˆãƒžã‚¯ãƒã‚¹F ~ã‚¤ãƒ„ãƒ¯ãƒªãƒŽã‚¦ã‚¿ãƒ’ãƒ¡â€ è¦‹ã¦ãã¾ã—ãŸ ã« ã¾ã£ãŸã‚“ ã‚ˆã‚Š æœ€è¿‘ã®æŠ•ç¨¿ ã€Žç¢ºçŽ‡çš„ç™ºæƒ³æ³•ã€ ãƒ™ã‚¤ã‚ºæŽ¨å®šã®ã‚¢ã‚¤ãƒ‡ã‚£ã‚¢ãŒä½•ã‚ˆã‚Šã‚ˆãã‚ã‹ã‚‹ï¼ ãƒ•ã‚©ãƒˆãƒªãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã®ã‚ã‚Œã“ã‚Œã€€ä»Šæ›´ã¾ãŸã‚„ã£ã¦ã¿ã¦ã„ã¾ã™ ã€ŽçµŒæ¸ˆã¯æ„Ÿæƒ…ã§å‹•ãã€ã€€è‰¯æ›¸ã ã‘ã©ãƒ»ãƒ»ãƒ» ã²ã¨ã‚Šã˜ã‚ƒãªã„ã‚ˆï½žãªãƒ—ãƒã‚°ãƒ©ãƒ ã€€Bridging Personal and Social Environment? è£½é€ æ¥ã®æ´¾é£åŠ´åƒç¦æ¢ã«ã¤ã„ã¦ç–‘å•ã«æ€ã†ã“ã¨ ã‚¢ãƒ¼
takado 2009/12/13
book

bayes

statistics
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https://www.cakra-slot88.com/
takado 2009/11/24
programming

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ã‚µãƒ¼ãƒ“ã‚¹çµ‚äº†ã®ãŠçŸ¥ã‚‰ã›
ã‚µãƒ¼ãƒ“ã‚¹çµ‚äº†ã®ãŠçŸ¥ã‚‰ã› ã„ã¤ã‚‚Yahoo! JAPANã®ã‚µãƒ¼ãƒ“ã‚¹ã‚’ã”åˆ©ç”¨ã„ãŸã ãèª ã«ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã™ã€‚ ãŠå®¢æ§˜ãŒã‚¢ã‚¯ã‚»ã‚¹ã•ã‚ŒãŸã‚µãƒ¼ãƒ“ã‚¹ã¯æœ¬æ—¥ã¾ã§ã«ã‚µãƒ¼ãƒ“ã‚¹ã‚’çµ‚äº†ã„ãŸã—ã¾ã—ãŸã€‚ ä»Šå¾Œã¨ã‚‚Yahoo! JAPANã®ã‚µãƒ¼ãƒ“ã‚¹ã‚’ã”æ„›é¡§ãã ã•ã„ã¾ã™ã‚ˆã†ã€ã‚ˆã‚ã—ããŠé¡˜ã„ã„ãŸã—ã¾ã™ã€‚
takado 2009/10/05
search

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ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã®å®ç®±
ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã«é–¢ã™ã‚‹ã“ã¨ã‚’ã€ãƒ‡ãƒ¼ã‚¿ã®é›†ã‚æ–¹ã‹ã‚‰çŸ¥è˜ã®ç²å¾—ã¾ã§ã€å¹…åºƒãä¸å¯§ã«è§£èª¬ã—ã¦ã„ã¾ã™ã€‚ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã§ç”¨ã„ã‚‰ã‚Œã‚‹çŸ¥è˜ç™ºè¦‹æŠ€è¡“ã¯ã€ãƒã‚¹ã‚±ãƒƒãƒˆåˆ†æžãƒ»æ±ºå®šæœ¨ãƒ»ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãªã©å¤šãã®æ‰‹æ³•ãŒã‚ã‚Šã¾ã™ã€‚ã ã‹ã‚‰ã¨ã„ã£ã¦å…¨éƒ¨ã‚’åŒæ™‚ã«åˆ©ç”¨ã™ã‚‹ã“ã¨ã¯è€ƒãˆã‚‰ã‚Œã¾ã›ã‚“ã€‚è§£æžæ‰‹æ³•ã¯ãã‚Œãžã‚Œã®å½¹å‰²ãŒã‚ã‚Šã€ãã®ç›®çš„ã«åˆã‚ã›ã¦ä½¿ã„åˆ†ã‘ãªã‘ã‚Œã°ãªã‚Šã¾ã›ã‚“ã€‚ ãã‚Œãžã‚Œã®å½¹å‰²ãŒã‚ã‚‹ã¨æ›¸ãã¾ã—ãŸãŒã€å¤§ããã‚ã‘ã‚‹ã¨ï¼’ã¤ã®ã‚¿ã‚¤ãƒ—ãŒã‚ã‚‹ã¨æ€ã„ã¾ã™ã€‚ã€Œãƒ‡ãƒ¼ã‚¿ã‹ã‚‰æœ¬è³ªã‚’æŽ¢ã‚‹ã€ã‚¿ã‚¤ãƒ—ã¨ã€Œãƒ‡ãƒ¼ã‚¿ã‹ã‚‰æœªçŸ¥ã®ç¾è±¡ã‚’äºˆæ¸¬ã™ã‚‹ã€ã‚¿ã‚¤ãƒ—ã§ã™ã€‚ ã“ã®ç« ã§ã¯ã€ã€Œãƒ‡ãƒ¼ã‚¿ã®æ´—æµ„ã€ã‹ã‚‰ã€ŒçŸ¥è˜ç™ºè¦‹ã®æ–¹æ³•ã€ã‚’äº¤ãˆã¦çŸ¥è˜ç™ºè¦‹ã‚’è€ƒãˆã¦ã¿ã¾ã™ã€‚
takado 2009/09/16
statistics

datamining

machinelearning
ãƒªãƒ³ã‚¯
Rasch model - Wikipedia
The Rasch model, named after Georg Rasch, is a psychometric model for analyzing categorical data, such as answers to questions on a reading assessment or questionnaire responses, as a function of the trade-off between the respondent's abilities, attitudes, or personality traits, and the it em difficulty.[1][2] For example, they may be used to estimate a student's reading ability or the extrem ity of
takado 2009/09/13
math

academic

algorithm

education

statistics
ãƒªãƒ³ã‚¯
Madagascar
takado 2009/08/21
software

statistics

image
ãƒªãƒ³ã‚¯
Machine Learning Project at the University of Waikato in New Zealand
Machine learning (ML) enables computers to analyse data and decide what information is most relevant, facilitating fast and accurate decision making. Our Objectives To develop improved algorithms for ML. To make ML techniques generally available. To apply ML to practical probl ems in Aotearoa. Software Staff and students at Waikato have developed a software workbench called Weka, for Waikato Enviro
takado 2009/08/20
programming

software

algorithm

statistics

library

machinelearning

datamining

ai
ãƒªãƒ³ã‚¯
support-vector-machines.org
This domain may be for sale!
takado 2009/08/04
programming

software

math

statistics

library

machinelearning

datamining

svm
ãƒªãƒ³ã‚¯
MCMC Preprint Service
MCMC Preprint Service Welcome to the MCMC Preprint Service This service provides a list of all registered papers on MCMC methodology currently submitted for publication. Please don't just browse, send us your papers too! Disclaimers It should be noted that the MCMC Preprint Service has no editorial policy. and that the administrators rely upon contributors to offer appropriate papers. It is enti
takado 2009/07/28
statistics

machinelearning

math

algorithm

paper

reference
ãƒªãƒ³ã‚¯
Link Analysis and Related Topics - Home
2008å¹´åº¦ å…ˆç«¯æƒ…å ±ç§‘å¦ç‰¹è«– II & IV ãƒªãƒ³ã‚¯è§£æžã¨å‘¨è¾ºã®è©±é¡Œ æ‹…å½“ æ–°ä¿ ä» [email protected]aist.jp æ—¥æ™‚ 2008/11/10, 11/17, 12/1, 12/8 (å…¨ 4 å›ž) - 4é™ 15:10-16:40 å ´æ‰€ æƒ…å ±æ£Ÿ L3 è¬›ç¾©å®¤ ãƒªãƒ³ã‚¯è§£æžã¯, ã‚°ãƒ©ãƒ• (ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯) ãƒ‡ãƒ¼ã‚¿ã®æ§‹é€ ã‹ã‚‰æœ‰ç”¨ãªæƒ…å ±ã‚’æŠ½å‡ºã™ã‚‹ãŸã‚ã®, ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã®ä¸€ç ”ç©¶åˆ†é‡Žã§ã™. ã“ã®è¬›ç¾©ã§ã¯ã¾ãš, ãƒªãƒ³ã‚¯è§£æžãŒå–ã‚Šæ‰±ã† 2 ç¨®é¡žã®å°ºåº¦ (é‡è¦åº¦ã¨é–¢é€£åº¦) ã«ã¤ã„ã¦è¿°ã¹, ãã‚Œãžã‚Œã®ä»£è¡¨çš„ãªè¨ˆç®—æ‰‹æ³•ã‚’ç´¹ä»‹ã—ã¾ã™. å¾ŒåŠã§ã¯, è¿‘å¹´æ©Ÿæ¢°å¦ç¿’åˆ†é‡Žã§ç››ã‚“ã«ç ”ç©¶ã•ã‚Œã¦ã„ã‚‹ã‚«ãƒ¼ãƒãƒ«ã®ã†ã¡, ã‚°ãƒ©ãƒ•ä¸Šã®ç¯€ç‚¹ã«å¯¾ã—ã¦å®šç¾©ã•ã‚ŒãŸã‚«ãƒ¼ãƒãƒ« (ã‚°ãƒ©ãƒ•ã‚«ãƒ¼ãƒãƒ«) ã¨, ãã®ãƒªãƒ³ã‚¯è§£æžã¸ã®å¿œç”¨ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¾ã™. ç¬¬1å›ž 11æœˆ10æ—¥ ã‚¹ãƒ©ã‚¤ãƒ‰ ç¬¬2å›ž 11æœˆ17æ—¥ ã‚¹ãƒ©ã‚¤ãƒ‰ ç¬¬3å›ž 12æœˆ1æ—¥
takado 2009/07/12
academic

presentation

algorithm

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math
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