ãããã¤ã³ã¹ãã¼ã«ä»£è¡¨ã®ã©ã¤ãããã¯ããã°
ICML/UAI/COLTã®accepted paperãåºæãããã¼ã£ã¨é¢ç½ãããªã®ãçã£ç«¯ããèªãã§ã¿ã¾ããã ICMLã®èªãã§ã¿ããèªãã§ã¿ãããªã¹ã ãã®ãã¡ããã¯ã¢ãããã¾ãã ICMLã¯å¼·åå¦ç¿ç³»ãå¤ããªã£ã¦ãããªãã¨ããæ°ãããã®ã§ããããã§ããªãããªã ã¤ãã§ã«ãç§ãèå³ãæã£ã¦ã¿ã¦ããæ©æ¢°å¦ç¿ã®å¦ä¼ï¼ã¨ä¸åã¸ã£ã¼ãã«ï¼ç´¹ä»ãããããå¢çé åãªã®ã§ä»ã®å¦ä¼ã§é¢ç½ã話ãçºè¡¨ãããããããã¨ãå¤ãã§ãã æ©æ¢°å¦ç¿ç³» JMLR Journal of Machine Learning Research æ©æ¢°å¦ç¿ã®ä¸çªã¡ã¸ã£ã¼ãªã¸ã£ã¼ãã«ã§åºãã¹ãã¼ããéãï¼ãã®å¹´ã«å¦ä¼çºè¡¨ããããã®ããã®å¹´ã®ãã¡ã«åºã¦ãããã¨ãçãããªãï¼ãå ¨é¨webä¸ããã¿ãã§è«æãè½ã¨ãããããã§ãã ICML International Conference on Machine Learning æ©æ¢°å¦ç¿
ããã > iKnow,è±èªå¦ç¿ > æå¼·Web2.0ãµã¼ãã¹ãiKnowãç»å ´ããã ãè±èªåå¼·ããã人ã ãï¼ ãããã«ã¾ããã®æ°åï¼HONDAãããã²ã¨ã¤ã®ãã¯ããã¸ã¼ãï½ã¤ã³ã¿ã¼ããÃããã°ãã¼ã¿ÃIoTÃéç½ï½ã01 ããã¯ã¡ãã«ã³ã³ãã¹ããå§ã¾ã£ãï½ï¼¨ï½ï½ï½ï½ãããã²ã¨ã¤ã®ãã¯ããã¸ã¼ã02ãï½ã¤ã³ã¿ã¼ããÃGPSÃã©ã¦ã³ãã¢ãã¦ãï½ãé転ãã人ããµãã¼ããããã¨ï½ï¼¨ï½ï½ï½ï½ãããã²ã¨ã¤ã®ãã¯ããã¸ã¼ã03ãï½ã¤ã³ã¿ã¼ããÃç½å®³æ å ±Ãã°ãããã¶ã¤ã³å¤§è³ï½ãéè¡å®ç¸¾æ å ±ããããã©ã¤ãã©ã¤ã³ã«ãªã£ãæ¥ 2007.11.27 ã¾ã å¹´ãè¶ãã¦ããªãã®ã«è¨ãã®ããªãã§ãããæ¥å¹´ã®ç®æ¨ã¯è±èªã ã£ãããã¾ãããããWritingã ãããªãã¨ãæã£ã¦ããã¨ãç¥æ§ã¨ããã®ã¯ããããã§ãdannychoo.comã§ãã¾ãã«ãããä»æ¬²ãããµã¼ãã¹ãå§ã¾ã£ã¦ãããã¨ãç´¹ä»ããã¦ãã¾ããã ãµã¼ãã¹ã®åå
ããã§ããâBã¯ã©ã¹âã£ã¦ããã®ã¯ã¹ã³ã¢ã730~860ã®ã¹ã³ã¢ã¬ã³ã¸ã®äºï¼860以ä¸ãAã¯ã©ã¹ï¼ãã¨ããããã¾ãã¯ãç°¡ç¥åããï¼ã¶æã®æµããæ©è¦ãã£ã¼ãã¨ãã¦ã¾ã¨ãã¨ãã¾ããä¸å¦çã¬ãã«ãã©ãã¨ããåã¹ãããã®è©³ç´°ãèæ¯çãªè©±ã¯ãã以éã«ã ãã®ä»TOEICé¢é£æ¸ç± åã¹ãããã®æ©è¦ãã£ã¼ãï¼è©³ç´°ã¯ãåã¹ãããã®è©³ç´°ããåç §ï¼ ï¼ï¼åºç¤ææ³åãã¤ãã åºç¤ä¸ã®åºç¤ã以ä¸ã®æ¬ã§ä¸æ°ã«ãããããããããªãã¨æ¬¡ã®åèªå¸³ã®ä¾æãç解ã§ããªãã 使ç¨æ¸ç±ï¼ TOEIC TESTææ³å®å ¨æ»ç¥ / ç³äº è¾°å æéï¼ï¼é±é ï¼ï¼åºç¤åèªåãã¤ãã æ¬æ°ã§ããããã ãã©ãã©ãã«ããã¦ï¼ã¶æã§DUO3.0ã®ä¾æãå®å ¨ã«ãã¹ã¿ã¼ããé³å£°ãå®å ¨ã«èãåããããã«ããã 使ç¨æ¸ç±ï¼DUO3.0 æéï¼ï¼ã1.5ã¶æï¼ï¼æ¥ï¼æéãããããã°ãããï¼ ï¼âAï¼Part5対ç ããã¯3-Bã¨å¹³è¡ãã¦ã¹ã¿ã¼ããæ¾åæ¬ã
èãæµãã®è±èªå¦ç¿ã ä»äºä¸ã«é³æ¥½ãè´ãã¦ããã®ã§ãããç¹ã«é³æ¥½ã好ãã¨ããããã¯ãè³æ ãããã§ãã ããã§ããã£ãããªãè±èªãèãã ãã§ãããããè±èªãèãåããããã«ãªããªãããªï¼ã¨èãã¦ãã¾ããå¹æçãªææã¯ããã¾ããï¼ ã¾ããããããã£ã¹ããªã©ã§ãç¡æã§å§ãããããã®ãããã°ãããããã§ãã http://www.nhk.or.jp/rj/index_e.html 以å¤ã§ãé¡ããã¾ãã ããããã£ã¹ãã§ITç³»ã®ãã¥ã¼ã¹ãè±èªã§èªã¿ä¸ãããã¦ãããããªãã®ã§ãããã§ãã ãã¡ãã¡ã¢ã¯ã·ã§ã³ããã®ã¯ãé¢åãªã®ã§ãèãæµãã§ã§ãããã®ã¿ãé¡ããã¾ãã
ããã¾ã§3åã«åãã¦ãããã使ã£ãè±èªåå¼·è¡ãç´¹ä»ãã¦ãã¾ããããèªãããèãããä¼è©±ãããã¨ããã°ãæå¾ã¯æãæ·å± ã®é«ããæ¸ããæè¡ââããªãã¡ã©ã¤ãã£ã³ã°ã§ãã å¤è³ç³»ã®ä¼æ¥ãæµ·å¤åãã®é¨ç½²ã«ãã人ã§ããªãéããæ» å¤ã«ä½¿ããã¨ã®ç¡ãã®ãè±èªã®ã©ã¤ãã£ã³ã°ã§ããæ å ±ãåãåãç«å ´ã§ããã°ãããã°ããèªãããããããã£ã¹ãã£ã³ã°ããèããããã¨ãã§ããã°æµ·å¤ã®æ å ±ã«ã¤ãã¦ãããã¨ãã§ãã¾ãããä»®ã«æµ·å¤åºå¼µãæ è¡ãããã¨ãã¦ããç¸æã¨ã話ãããã¨ãã§ããã°è±èªãæ¸ããªãã¦ãããã»ã©è¦å´ãããã¨ã¯ããã¾ããã ã¨ã¯ãããè±èªãæ¸ããã¨ãã§ããã°ãä¸çä¸ã®äººã¨ã¡ã¼ã«ã§ã³ãã¥ãã±ã¼ã·ã§ã³ãã¨ã£ãããæ å ±çºä¿¡ãããã¨ãå¯è½ã«ãªãã¾ããå¦ã¶ã®ã¯å¤§å¤ã§ããããã®åãææ¦ãã価å¤ã®ããæè¡ã¨è¨ããã§ããããä»åã¯ããããªè±èªã®ã©ã¤ãã£ã³ã°ãåå¼·ããæ¹æ³ãããç´¹ä»ãããã¨æãã¾ãã âã¤ã³ã¿ã¼ãããæ代ã®è±
ãªãªã¼ã¹ãé害æ å ±ãªã©ã®ãµã¼ãã¹ã®ãç¥ãã
ææ°ã®äººæ°ã¨ã³ããªã¼ã®é ä¿¡
å¦çãå®è¡ä¸ã§ã
j次ã®ããã¯ãã¼ã¯
kåã®ããã¯ãã¼ã¯
lãã¨ã§èªã
eã³ã¡ã³ãä¸è¦§ãéã
oãã¼ã¸ãéã
{{#tags}}- {{label}}
{{/tags}}