[B! æ©Ÿæ¢°å¦ç¿’] TaKUMAã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

TaKUMA id:TaKUMA

æ©Ÿæ¢°å¦ç¿’ã«é–¢ã™ã‚‹TaKUMAã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (16)

${{author_name}}$

{{{comment_expanded}}}

{{label}}

{{#is_bookmark}}ãƒªã‚¹ãƒˆ{{/is_bookmark}}{{^is_bookmark}}ãƒªãƒ³ã‚¯{{/is_bookmark}}

${{author_name}}$
{{author_name}}{{created}}
{{ #comment }}{{ comment }}{{ /comment }}
- {{ label }}

${{author_name}}$

{{{comment_expanded}}}

{{label}}

{{#is_bookmark}}ãƒªã‚¹ãƒˆ{{/is_bookmark}}{{^is_bookmark}}ãƒªãƒ³ã‚¯{{/is_bookmark}}

ã‚¼ãƒã‹ã‚‰å§‹ã‚ã‚‹DeepLearning_ãã®1_ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã¨ã¯ - åˆ†ã‹ã‚‰ã‚“ã“ã¨å¤šã™ãŽ
å¯¾è±¡ã¨ã™ã‚‹äºº ãƒ‡ã‚£ãƒ¼ãƒ—ãƒ©ãƒ¼ãƒ‹ãƒ³ã‚°ã™ã”ã„ï¼ã€€â†èžãé£½ããŸ ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã‚ã‚‹ã‚ˆï¼ã€€â†ãµã‚ã£ã¨ã—ã™ãŽã¦å…·ä½“çš„ãªã¨ã“ã‚ãŒåˆ†ã‹ã‚‰ã‚“ ã“ã†ã„ã†è«–æ–‡ã‚ã‚‹ã‚ˆï¼ã€€â†èªã‚ã‚‹ã‚ã‘ãªã„ã ã‚ ãã†ã„ã†äººå‘ã‘ã€‚ï¼ˆãŸã¶ã‚“å¦éƒ¨å››å¹´ç¨‹åº¦å‘ã‘ï¼‰ ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆåˆå¦è€…ãŒã€æ›¸ããã‚‹ã¾ã§æ€ªã—ã„ã¨ã“ã‚æº€è¼‰ã§ã‚‚çªã£èµ°ã‚Šã¾ã™ã€‚ ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆï¼ˆã“ã®è¨˜äº‹ï¼‰ â†’ï¼ˆAutoEncoderï¼‰ â†’ï¼ˆDenoisingAutoEncoderï¼‰ â†’ãƒ›ãƒƒãƒ—ãƒ•ã‚£ãƒ¼ãƒ«ãƒ‰ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ â†’ãƒœãƒ«ãƒ„ãƒžãƒ³ãƒžã‚·ãƒ³ â†’Restrictedãƒœãƒ«ãƒ„ãƒžãƒ³ãƒžã‚·ãƒ³ â†’ï¼ˆGaussian Binary - Restricted Boltzmann Machinesï¼‰ â†’ï¼ˆDeepBeliefNetworkï¼‰ â†’ï¼ˆDeepNeuralNetworksï¼‰ â†’ç•³ã¿è¾¼ã¿ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆï¼ˆå¾Œæ—¥ï¼‰ ã¾ã§ã‚„ã‚‹ã€‚ å¤ªç·šä»¥å¤–ã¯èªã¿é£›ã°ã—ã¦OK æœ¬æ–‡ä¸ã§ã¯æ€–ã„å¼ã¯ä½¿ã‚ãªã„ã€‚ï¼ˆAppendixã«æ›¸
TaKUMA 2014/05/21
æ©Ÿæ¢°å¦ç¿’

R
ãƒªãƒ³ã‚¯
GitHub - shuyo/iir: Machine Learning / Natural Language Processing / Information Retrieval
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
TaKUMA 2014/03/18
æ©Ÿæ¢°å¦ç¿’
ãƒªãƒ³ã‚¯
Data clustering: 50 years beyond K-means
TaKUMA 2013/09/04
K-means

æ©Ÿæ¢°å¦ç¿’
ãƒªãƒ³ã‚¯
Introduction to Machine Learning
Introduction to Machine Learning Draft of Incomplete Notes by Nils J. Nilsson [email protected] http://ai.stanford.edu/~nilsson Description (as of February 15, 2015): From this page you can download a draft of notes I used for a Stanford course on Machine Learning. Although I have tried to eliminate errors, some undoubtedly rem ain---caveat lector. Certain elements of the typography (overflow
TaKUMA 2012/12/14
æ•™ç§‘æ›¸

æ©Ÿæ¢°å¦ç¿’
ãƒªãƒ³ã‚¯
PRML ã®èªã‚€ç« ãƒ»é£›ã°ã™ç« (ç§å®¶ç‰ˆ) - æœ¨æ›œä¸è¶³
æ©Ÿæ¢°å¦ç¿’ã®å®šç•ªæ•™ç§‘æ›¸ã®1ã¤ã¨è¨€ã‚ã‚Œã€å„åœ°ã§èªæ›¸ä¼šãŒé–‹ã‹ã‚Œã‚‹ã€Œãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ã¨æ©Ÿæ¢°å¦ç¿’ã€(PRML)ã€‚èªã¿è§£ãã«ã¯ã‚ã‚‹ç¨‹åº¦ã®è§£æžã¨ç·šå½¢ä»£æ•°ã®çŸ¥è˜ãŒå¿…è¦ãªãŸã‚ã€æ•°å¦ãŒè‹¦æ‰‹ãªå¦ç”Ÿã•ã‚“ã‚„Ã—Ã—å¹´ã¶ã‚Šã«æ•°å¼ã‚’ç›®ã«ã—ãŸã¨ã„ã†ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ãŸã¡ã‚’æ¬¡ã€…ã¨ã€Œå¼å¤‰å½¢ã§ããªã„â€¦â€¦ã€ã¨ã„ã†å¥ˆè½ã«å©ãè¾¼ã‚“ã§ã„ã‚‹ã¨ã„ã†ã€‚ ã‚µã‚¤ãƒœã‚¦ã‚ºãƒ»ãƒ©ãƒœã®ç¤¾å†… PRML èªæ›¸ä¼šã§ã‚‚ãã®ç¾è±¡ãŒç™ºç”Ÿã€‚è¦‹ã‹ããŸåŒåƒšã®å…‰æˆã•ã‚“ãŒ PRML ã§ä½¿ã‚ã‚Œã¦ã„ã‚‹æ•°å¦ã®è§£èª¬ã ã‘ã§ã¯ãªãã€PRML ã®ä¸ã§çœç•¥ã•ã‚Œã¦ã„ã‚‹å¼å¤‰å½¢ã®éŽç¨‹ã‚‚å«ã‚ã¦æ›¸ãä¸‹ã—ãŸãƒ¡ãƒ¢ï¼ˆç¤¾å†…é€šç§°ï¼šã‚¢ãƒ³ãƒãƒ§ã‚³ï¼‰ãŒæš—é»’é€šä¿¡å›£ã‹ã‚‰ã€Œæ©Ÿæ¢°å¦ç¿’ã¨ãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ã®å¦ç¿’ã€ã¨ã„ã†åŒäººèªŒã¨ã—ã¦å‡ºç‰ˆã•ã‚Œã€å…¨å›½ã®ã‚¸ãƒ¥ãƒ³ã‚¯å ‚ã§è³¼å…¥å¯èƒ½ã¨ãªã‚‹ã¨ã¡ã‚‡ã£ã¨ã—ãŸãƒ ãƒ¼ãƒ–ãƒ¡ãƒ³ãƒˆãŒãŒãŒã€‚ ç¾åœ¨ã¯ã‚¢ãƒžã‚¾ãƒ³ã§ã‚‚è³¼å…¥å¯èƒ½ã¨ãªã£ã¦ã„ã‚‹ãŒã€ã‚‚ã¨ã‚‚ã¨ã®ã‚¢ãƒ³ãƒãƒ§ã‚³ã‚‚ PDF ã§ç„¡æ–™å…¬é–‹(CC-BY ãƒ©ã‚¤ã‚»ãƒ³ã‚¹)ã•ã‚Œã¦ã„ã‚‹ã®ã§ã€ç´™ã®æœ¬ã§ãªã„ã¨å‹‰å¼·ã™
TaKUMA 2012/12/11
PRML

æ©Ÿæ¢°å¦ç¿’

æœ¬
ãƒªãƒ³ã‚¯
æ©Ÿæ¢°å¦ç¿’ ã¯ã˜ã‚ã‚ˆã† è¨˜äº‹ä¸€è¦§ | gihyo.jp
é‹å–¶å…ƒã®ãƒã‚´ Copyright Â© 2007-2024 All Rights Reserved by Gijutsu-Hyoron Co., Ltd. ãƒšãƒ¼ã‚¸å†…å®¹ã®å…¨éƒ¨ã‚ã‚‹ã„ã¯ä¸€éƒ¨ã‚’ç„¡æ–ã§åˆ©ç”¨ã™ã‚‹ã“ã¨ã‚’ç¦æ¢ã—ã¾ã™â ã€‚å€‹åˆ¥ã«ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ãŒè¨å®šã•ã‚Œã¦ã„ã‚‹è¨˜äº‹ç‰ã¯ãã®ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã«å¾“ã„ã¾ã™ã€‚
TaKUMA 2012/07/08
æ©Ÿæ¢°å¦ç¿’

ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ

Programming
ãƒªãƒ³ã‚¯
ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã§ä½¿ã‚ã‚Œã‚‹ãƒˆãƒƒãƒ—10ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ - ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆä¸ŠãŒã‚Šã®DXå‚è¬€ãƒ»èµ·æ¥å®¶
2006å¹´ã®ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°å¦ä¼šã€IEEE ICDMã§é¸ã°ã‚ŒãŸã€Œãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã§ä½¿ã‚ã‚Œã‚‹ãƒˆãƒƒãƒ—10ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã€ã«æ²¿ã£ã¦æ©Ÿæ¢°å¦ç¿’ã®æ‰‹æ³•ã‚’ç´¹ä»‹ã—ã¾ã™ï¼ˆã“ã®è«–æ–‡ã¯@doryokujinå›ã®ãƒã‚¹ãƒˆã§çŸ¥ã‚Šã¾ã—ãŸã€ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã™ï¼ï¼‰ã€‚ å¿…ãšã—ã‚‚è«–æ–‡ã®å†…å®¹ã«ã¯æ²¿ã£ã¦ãŠã‚‰ãšå€‹äººçš„ãªç§è¦‹ã‚‚å…¥ã£ã¦ã„ã¾ã™ã®ã§ã€è©³ç´°ã¯åŽŸè«–æ–‡ã‚’ã”ç¢ºèªä¸‹ã•ã„ã€‚ã¾ãŸã€ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã®å…¨ä½“è¦³ã‚’ã‚µãƒ¼ãƒ™ã‚¤ã—ãŸã‚¹ãƒ©ã‚¤ãƒ‰è³‡æ–™ãŒã‚ã‚Šã¾ã™ã®ã§ã€ã“ã¡ã‚‰ã‚‚ä½µã›ã¦ã”è¦§ä¸‹ã•ã„ã€‚ ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã®åŸºç¤Ž View more presentations from Issei Kurahashi 1. C4.5 C4.5ã¯CLSã‚„ID3ã¨ã„ã£ãŸã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’æ”¹è‰¯ã—ã¦ã§ããŸã‚‚ã®ã§ã€æ±ºå®šæœ¨ã‚’ä½¿ã£ã¦åˆ†é¡žå™¨ã‚’ä½œã‚Šã¾ã™ã€‚æ±ºå®šæœ¨ã¨ã„ãˆã°CARTãŒè‰¯ãä½¿ã‚ã‚Œã¾ã™ãŒã€CARTã¨ã®é•ã„ã¯ä»¥ä¸‹ã®ã¨ãŠã‚Šã§ã™ã€‚ CARTã¯2åˆ†å²ã—ã‹ã§ããªã„ãŒC4.5ã¯3åˆ†å²ä»¥ä¸Šã‚‚ã§ãã‚‹ C
TaKUMA 2011/11/24
data

ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°

ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ

è«–æ–‡

ã¾ã¨ã‚

æ©Ÿæ¢°å¦ç¿’
ãƒªãƒ³ã‚¯
æ§‹é€ ãƒ‡ãƒ¼ã‚¿ã‹ã‚‰ã®æ©Ÿæ¢°å¦ç¿’ - ãƒ‡ãƒ¼ã‚¿è§£æžæŠ€è¡“
å¥³æ€§ãŒç”ŸæˆAIã®æ´»ç”¨ã‚’ç‰½å¼•ã—ã¦æœªæ¥ã‚’æ‹“ã ãƒ“ã‚¸ãƒã‚¹ã‚’ä¸€å¤‰ã•ã›ã¤ã¤ã‚ã‚‹ç”ŸæˆAIã€‚å¥³æ€§ãŒå…ˆé§†è€…ã¨ãªã‚‹ã“ã¨ã§å®Ÿç¾ã§ãã‚‹ä¸–ç•ŒãŒã‚ã‚Šã¾ã™ã€‚ è©³ç´°ãƒ¬ãƒãƒ¼ãƒˆã‚’å…¥æ‰‹ ã“ã®ãŸã³ã®ä»¤å’Œ6å¹´èƒ½ç™»åŠå³¶åœ°éœ‡ã§è¢«ç½ã•ã‚ŒãŸçš†æ§˜ã«è¬¹ã‚“ã§ãŠè¦‹èˆžã„ç”³ã—ä¸Šã’ã¾ã™ã€‚ ä»¤å’Œ6å¹´1æœˆ1æ—¥ã«ç™ºç”Ÿã—ãŸèƒ½ç™»åŠå³¶åœ°éœ‡ã«ã‚ˆã‚Šè¢«ç½ã•ã‚ŒãŸãŠå®¢æ§˜å‘ã‘ã®ä¿å®ˆã‚µãƒ¼ãƒ“ã‚¹ç‰¹åˆ¥å¯¾å¿œ ã‚·ã‚¹ãƒ†ãƒ é–‹ç™ºã‚„é‹ç”¨ã«ç”ŸæˆAIã‚’æ´»ç”¨ã™ã‚‹ã€ŒITå¤‰é©ã®ãŸã‚ã®AIã‚½ãƒªãƒ¥ãƒ¼ã‚·ãƒ§ãƒ³ã€ã«ã‚ˆã‚Šã€ç”Ÿç”£æ€§ã¨å“è³ªã®å‘ä¸Šã‚’å®Ÿç¾
TaKUMA 2011/11/19
Technology

Science

graph

data

æ©Ÿæ¢°å¦ç¿’

è«–æ–‡

CS
ãƒªãƒ³ã‚¯
PRML - æœ±é·ºã®æœWikiã€€ãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ã¨æ©Ÿæ¢°å¦ç¿’ - ãƒ™ã‚¤ã‚ºç†è«–ã«ã‚ˆã‚‹çµ±è¨ˆçš„äºˆæ¸¬
ãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ã¨æ©Ÿæ¢°å¦ç¿’ - ãƒ™ã‚¤ã‚ºç†è«–ã«ã‚ˆã‚‹çµ±è¨ˆçš„äºˆæ¸¬â€ This is a support page for the Japanese edition of "Pattern Recognition and Machine Learning" authored by C. M. Bishop. æœ¬æ›¸ã¯ï¼ŒChristopher M. Bishop è‘—ã€ŒPattern Recognition and Machine Learningã€ã®æ—¥æœ¬èªžç‰ˆã§ã™ï¼Žä¸Šä¸‹2å·»ã®æ§‹æˆã§ã™ï¼Ž ãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ã‚„æ©Ÿæ¢°å¦ç¿’ã®å„ç¨®ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚„èƒŒå¾Œã®è€ƒãˆã«ã¤ã„ã¦ï¼Œãƒ™ã‚¤ã‚ºç†è«–ã®è¦³ç‚¹ã‹ã‚‰è§£èª¬ã—ãŸæ•™ç§‘æ›¸ã§ã™ï¼Ž åŸºç¤Žçš„ãªç·šå½¢ãƒ¢ãƒ‡ãƒ«ã‹ã‚‰ï¼Œã‚«ãƒ¼ãƒãƒ«ãƒˆãƒªãƒƒã‚¯ï¼Œã‚°ãƒ©ãƒ•ã‚£ã‚«ãƒ«ãƒ¢ãƒ‡ãƒ«ï¼ŒMCMCãªã©ã®ç™ºå±•çš„ãªè©±é¡Œã¾ã§ã‚’ãƒãƒ©ãƒ³ã‚¹è‰¯ãåŽéŒ²ã—ã¦ã„ã¾ã™ï¼Ž æ•°å¼ã«ã‚ˆã‚‹å½¢å¼çš„ãªè¨˜è¿°ã ã‘ã«ã¨ã©ã¾ã‚‰ãšï¼Œè±Šå¯Œãªã‚«ãƒ©ãƒ¼ã®å›³ã«ã‚ˆã‚‹ç›´è¦³çš„ãªèª¬æ˜Žã‚‚ãªã•ã‚Œã¦ã„ã¾ã™ï¼Ž æœ¬
TaKUMA 2011/01/18
æ©Ÿæ¢°å¦ç¿’

ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ

æœ¬
ãƒªãƒ³ã‚¯
æœ‰åã©ã“ã‚ãªæ©Ÿæ¢°å¦ç¿’æ‰‹æ³•ã®å¹´è¡¨ - æœ¨æ›œä¸è¶³
ã¡ã‚‡ã£ã¨æ©Ÿæ¢°å¦ç¿’ã®æ¯”è¼ƒçš„æœ‰åãªãƒ¢ãƒ‡ãƒ«ã‚„ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã®åˆå‡ºã«ã¤ã„ã¦å¹´è¡¨ã‚’ä½œã£ã¦ã¿ãŸã€‚ ã£ã¦ä»Šé€±æœ«ç”¨ã®è³‡æ–™ãªã‚“ã ã‘ã©ãï½— 1805 Method of Least Squares 1901 PCA (Principal Component Analysis) 1905 Random Walk -1925 Logistic Regression 1936 Fisher's Linear Discriminant Analysis 1946 Monte Carlo Method 1948 n-gram model 1950 RKHS (Reproducing Kernel Hilbert Space) 1950s Markov Decision Process -1957 Perceptron 1958 Kalman Filter 1960s Hidden Markov Model -1961 N
TaKUMA 2011/01/14
æ©Ÿæ¢°å¦ç¿’

ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ

Programming

ç ”ç©¶
ãƒªãƒ³ã‚¯
Apache Mahout - Overview
For Creating Scala ble Performant Machine Learning Applications Download Mahout Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end, or can be extended to other distributed backe
TaKUMA 2010/07/17
Hadoop

æ©Ÿæ¢°å¦ç¿’

Technology
ãƒªãƒ³ã‚¯
Weka 3 - Data Mining with Open Source Machine Learning Software in Java
Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued
TaKUMA 2010/07/17
æ©Ÿæ¢°å¦ç¿’

tool

source

Java
ãƒªãƒ³ã‚¯
https://cs229.stanford.edu/materials.html
TaKUMA 2010/07/17
CS

Lecture

æ©Ÿæ¢°å¦ç¿’
ãƒªãƒ³ã‚¯
https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599
TaKUMA 2010/07/17
YouTube

å‹•ç”»

English

æ©Ÿæ¢°å¦ç¿’

Stanford
ãƒªãƒ³ã‚¯
BLOG::broomie.net: æ©Ÿæ¢°å¦ç¿’ã®å‹‰å¼·ã‚’å§‹ã‚ã‚‹ã«ã¯
thriftã¨ã‹hadoopãªã©ï¼Œä½•ã‚„ã‚‰ã„ã‚ã„ã‚ã¨æ‰‹ã‚’å‡ºã—ã¦ã—ã¾ã„ï¼Œã“ã“ã®ã¨ã“ã‚ãƒ–ãƒã‚°ã®æ›´æ–°ãŒæ»žã£ã¦ã—ã¾ã£ã¦ã„ã¾ã™ãŒï¼Œä»Šæ—¥ã¯å‰ã‹ã‚‰æ›¸ããŸã‹ã£ãŸãƒˆãƒ”ãƒƒã‚¯ã«ã¤ã„ã¦è‡ªåˆ†ã¸ã®ãƒ¡ãƒ¢ã®æ„å‘³ã‚‚å«ã‚ã¦è¨˜ã—ã¦ãŠããŸã„ã¨æ€ã„ã¾ã™ï¼Ž ã¯ã˜ã‚ã« æœ€è¿‘ï¼Œã¨ã„ã£ã¦ã‚‚çµæ§‹å‰ã‹ã‚‰ãªã®ã§ã™ãŒï¼Œæµ·å¤–ã®ãƒ–ãƒã‚°ãªã©ã§ã€Œæ©Ÿæ¢°å¦ç¿’ã®å‹‰å¼·ã‚’å§‹ã‚ã‚‹ã‚¬ã‚¤ãƒ‰ãƒ©ã‚¤ãƒ³ã€ã«ã¤ã„ã¦ã®ã‚¨ãƒ³ãƒˆãƒªãƒ¼ãŒã„ãã¤ã‹è¦‹ã‚‰ã‚Œï¼Œã‹ã¤ï¼Œè°è«–ã‚‚å°‘ã—ç››ã‚Šä¸ŠãŒã£ã¦ã„ã¾ã™ï¼Žåƒ•ã¯æ©Ÿæ¢°å¦ç¿’ãŒå¥½ããªã ã‘ã§ï¼Œå°‚é–€ã¨ã„ã†ã«ã¯ã»ã©é ã„ã®ã§ã™ãŒï¼Œåƒ•ã‚‚ä¸€åˆ©ç”¨è€…ã¨ã—ã¦ã¯ã“ã®ãƒˆãƒ”ãƒƒã‚¯ã«é–¢ã—ã¦ã¯ã¨ã¦ã‚‚èˆˆå‘³ãŒã‚ã‚Šã¾ã™ï¼Ž æ©Ÿæ¢°å¦ç¿’ã¨ã„ã†ã¨ï¼Œè‰²ã€…ãªæ•°å¦çš„ãªçŸ¥è˜ãŒå¿…è¦ã§ã‚ã£ãŸã‚Šï¼Œçµ±è¨ˆå¦ã‚„äººå·¥çŸ¥èƒ½ã®çŸ¥è˜ã‚‚å¿…è¦ã«ãªã£ãŸã‚Šã—ã¾ã£ãŸã‚Šã¨ï¼Œå°‚é–€çš„ã«å¦ã¶æ©Ÿä¼šãŒç„¡ã‹ã£ãŸäººã«ã¨ã£ã¦ã¯èˆˆå‘³ãŒæ¹§ã„ã¦ã‚‚ãªã‹ãªã‹å§‹ã‚ã‚‹ã«ã¯å°»è¾¼ã¿ã—ã¦ã—ã¾ã†ã“ã¨ã‹ã¨æ€ã„ã¾ã™ï¼Žä»Šæ—¥ç´¹ä»‹ã™ã‚‹ã‚¨ãƒ³ãƒˆãƒªãƒ¼ã¯ï¼Œãã‚“ãªæ–¹ã€…ã«ãƒ’ãƒ³ãƒˆã«ãªã‚‹ã‚ˆã†ãªå†…å®¹ã«ãªã£ã¦ã„ã‚‹ã¨
TaKUMA 2010/07/06
å‹‰å¼·

æ©Ÿæ¢°å¦ç¿’

blog
ãƒªãƒ³ã‚¯
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
TaKUMA 2009/10/16
æ•™ç§‘æ›¸

æ©Ÿæ¢°å¦ç¿’

CS
ãƒªãƒ³ã‚¯
1