Simple real-time large-scale machine learning infrastructure.
Stochastic Average Gradient (SAG)ã¯NIPS 2012ã§ææ¡ãããæ°ããæé©åææ³ã§ãããç®çé¢æ°ãstrongly convexã§ããå ´åãã¨ããæ¡ä»¶ä»ãã§ã¯ããããç·å½¢åæãä¿è¨¼ããã¦ãããè¦ããã«ãéãã A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Setsãè«æã®è§£èª¬ã«ã¤ãã¦ã¯Oiwaç¥ã®è¨äºãåç §ããã¨è¯ãã¨æãã以ä¸ã§ã¯ãSAGã®èãæ¹ã«ã¤ãã¦ãä¸è¬çãªSGDã¨ã®å·®ç°ãä¸å¿ã«èª¬æãããã SGDã®å¾©ç¿ãSAGã説æããåã«ãSGDï¼Stochastic Gradient Descentã確ççå¾é éä¸æ³ï¼ã¨ã¯ã©ããªææ³ã ã£ããã確èªãã¦ãããã SGDã¯ã©ã³ãã ã«1ã¤ã®ãã¼ã¿ãåã£ã¦ãã¦gradientãè¨ç®ãããã®gr
1. Jubatusã«ããã⼤大è¦æ¨¡åæ£ ãªã³ã©ã¤ã³æ©æ¢°å¦ç¿ 2011/12/08 @⼤大è¦æ¨¡ãã¼ã¿å¦ç理åå¼·ä¼ æ ªå¼ä¼ç¤¾Preferred Infrastructure æµ·éâãè£ä¹ (@unnonouno) 2. â¾èªâ¼°å·±ç´¹ä» lï¬â¯ æµ·éâãè£ä¹ (@unnonouno) lï¬â¯ Preferred Infrastructure (PFI) ç 究éçºé¨â¾¨éãªãµã¼ãã£ã¼ lï¬â¯ 社å¡20â¼äººããã lï¬â¯ æ¤ç´¢ï¥ªã»ã¬ã³ã¡ã³ãã¨ã³ã¸ã³Sedueã®éçºãªã© lï¬â¯ å°â¾¨é lï¬â¯ â¾èªç¶â¾è¨èªå¦ç理 lï¬â¯ ããã¹ããã¤ãã³ã° lï¬â¯ Jubatusããã¸ã§ã¯ãå ã§ã®å½¹å² lï¬â¯ 主ã«ç¹å¾´æ½åºã¨ã³ã¸ã³ãæ©æ¢°å¦ç¿ã¨ã³ã¸ã³ã®ç 究éçº 2 3. Big Data ! lï¬â¯ ãã¼ã¿ã¯ãããããå¢å ãç¶ãã å¤ããã¨ããå¢ãã¦ããã¨ãããã¨ãéè¦ lï¬ ãã¼ã¿é量ã®å¤åã«å¯¾å¿ã§ããã¹ã±ã¼ã©ãã«ãªã·
å 容ã¯ç·å½¢èå¥ã¢ãã«ã®å¦ç¿ã«ã¤ãã¦ï¼Perceptron, PA, CW, AROW, NHELDã¨NLP2010ã®tutorial + ææ°ã®ã¢ãããã¼ã. æ´æ°å¼ãæ´çããã¦ãã¾ãï¼ããªã³ã©ã¤ã³å¸æé©åã®regret解æãsublinearãªSVMã®å¦ç¿ã®è©±ã§ããæè¿å ¬éããjubatusã®ä¸ã®å¦ç¿ã¢ã«ã´ãªãºã ã®è§£èª¬ã§ãããã¾ãã ã³ã¹ãé¢æ°ãå¸ã§ããå ´åã®Online Gradient Descentã®regret解æã®è¨¼æã¯ç¾ããã£ãã®ã§ãæ®éã¯ããããã®ã¯ãã¬ã¼ã³ã§ã¯ãããªãã¨ãããã®ã§ããç´¹ä»ãã¾ããã Sublinearã®å¦ç¿ã®è©±ã¯ä»å¾ããããçºå±ãããã§ããåå¦ç¿ä¾ã«åçã«éã¿ãã¤ãã¦åªå çã«å¦ç¿ããæ¹æ³ã¯ç´æçã«ã¯ã§ãããã ã¨æèãã¦ãã®ã§ãããããããå½¢ã§ãããã«å®å¼åã§ããã®ã ã¨æå¿ãã¾ããã IBISã¯ããããåå ãã¦ãã¾ãããæ¯å¹´æ°ããåéã®åé¡ãç»å ´ãã¦ãã¦é¢ç½
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}}