[B! algorithm] haidaã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

haida id:haida

algorithmã«é–¢ã™ã‚‹haidaã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (10)

${{author_name}}$

{{{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}}

k-meansæ³• - æ©Ÿæ¢°å¦ç¿’ã®ã€Œæœ±é·ºã®æœWikiã€
k-meansæ³• (k-means method)â€ æ¬¡ã®ç›®çš„é–¢æ•°ã‚’æœ€å°åŒ–ã™ã‚‹åˆ†å‰²æœ€é©åŒ–ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã®ä»£è¡¨çš„æ‰‹æ³•ï¼Ž \[\mathrm{Err}(\{X_i\})=\sum_i^k\;\sum_{\mathbf{x}\in X_i}\;{\|\mathbf{x} - \bar{\mathbf{x}}_i\|}^2\] ãŸã ã—ï¼Œãƒ‡ãƒ¼ã‚¿é›†åˆ $X$ ã¯ï¼Œãƒ™ã‚¯ãƒˆãƒ«ã§è¡¨ç¾ã•ã‚ŒãŸãƒ‡ãƒ¼ã‚¿ $\mathbf{x}$ ã®é›†åˆï¼Ž ã‚¯ãƒ©ã‚¹ã‚¿ $X_i$ ã¯ï¼Œãƒ‡ãƒ¼ã‚¿é›†åˆã®ç¶²ç¾…çš„ã§äº’ã„ã«ç´ ãªéƒ¨åˆ†é›†åˆï¼Ž $\bar{\mathbf{x}}_i$ ã¯ $X_i$ ä¸ã®é‡å¿ƒ(ã‚»ãƒ³ãƒˆãƒã‚¤ãƒ‰ã¨ã‚‚ã„ã†)ï¼Ž $\|\cdot\|$ ã¯ãƒ¦ãƒ¼ã‚¯ãƒªãƒƒãƒ‰ãƒŽãƒ«ãƒ ï¼Ž â†‘ ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ â€ å…¥åŠ›ã¯ãƒ‡ãƒ¼ã‚¿é›†åˆ $X$ ã¨ã‚¯ãƒ©ã‚¹ã‚¿æ•° $k$ï¼ŒãŠã‚ˆã³æœ€å¤§åå¾©æ•° maxIterï¼Ž åˆæœŸåŒ–ï¼šãƒ‡ãƒ¼ã‚¿é›†åˆã‚’ãƒ©ãƒ³ãƒ€ãƒ ã« \(
haida 2008/08/25
algorithm
ãƒªãƒ³ã‚¯
venture-skills.co.uk
haida 2008/08/20
algorithm

sns
ãƒªãƒ³ã‚¯
è‘—åã‚½ãƒ¼ã‚·ãƒ£ãƒ«ãƒ¡ãƒ‡ã‚£ã‚¢ãŒä½¿ã£ã¦ã„ã‚‹ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’å¤§å…¬é–‹ï¼ | Moz - SEOã¨ã‚¤ãƒ³ãƒã‚¦ãƒ³ãƒ‰ãƒžãƒ¼ã‚±ãƒ†ã‚£ãƒ³ã‚°ã®å®Ÿè·µæƒ…å ±
â€œã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ â€ã¯ã€ã‚‚ã£ã¨ã‚‚éžäººé–“çš„ãªã‚‚ã®ã®ä»£è¡¨ã ã¨ã‚‚ã„ãˆã‚‹ã€‚ã‚½ãƒ¼ã‚·ãƒ£ãƒ«ãƒ¡ãƒ‡ã‚£ã‚¢ã«ã¨ã£ã¦ã€ãã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãŒä¸å¯æ¬ ã ã¨ã„ã†ã®ã¯ã€å®Ÿã«çš®è‚‰ã‚ã„ã¦ã„ã‚‹ã€‚ åƒ•ã¯ã“ã®é–“ã€ã‚°ãƒ¼ã‚°ãƒ«ãŒã©ã†ã‚„ã£ã¦ãƒ¦ãƒ¼ã‚¶ãƒ¼ãƒ‡ãƒ¼ã‚¿ã‚’é›†ã‚ã¦ã„ã‚‹ã‹ã«ã¤ã„ã¦æ›¸ã„ãŸè¨˜äº‹ã‚’æŽ²è¼‰ã—ãŸï¼ˆå‰ç·¨ã€å¾Œç·¨ï¼‰ã€‚ä»Šå›žã¯ã€è‘—åãªã‚½ãƒ¼ã‚·ãƒ£ãƒ«ãƒ¡ãƒ‡ã‚£ã‚¢ã‚µã‚¤ãƒˆãŒã€ãƒ¦ãƒ¼ã‚¶ãƒ¼ãƒ‡ãƒ¼ã‚¿ã‚’æ´»ç”¨ã™ã‚‹ä¸Šã§ã©ã®ã‚ˆã†ã«ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ç”¨ã„ã¦ã„ã‚‹ã®ã‹ã€ç™½æ—¥ã®ä¸‹ã«ã•ã‚‰ãã†ã€‚ ã‚½ãƒ¼ã‚·ãƒ£ãƒ«ãƒ¡ãƒ‡ã‚£ã‚¢ã‚’æˆã‚Šç«‹ãŸã›ã¦ã„ã‚‹ã®ã¯äººé–“ã®åŠ›ã ãŒã€ãƒ¦ãƒ¼ã‚¶ãƒ¼ãŒå…¥åŠ›ã—ãŸãƒ‡ãƒ¼ã‚¿ã‚’åˆ©ç”¨ã§ãã‚‹çŠ¶æ…‹ã«ã™ã‚‹ä»•çµ„ã¿ã¯ã€ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«ã‚ˆã£ã¦ä½œã‚‰ã‚Œã¦ã„ã‚‹ã€‚ç¾åœ¨æ´»å‹•ã—ã¦ã„ã‚‹ç„¡æ•°ã®ã‚½ãƒ¼ã‚·ãƒ£ãƒ«ãƒ¡ãƒ‡ã‚£ã‚¢ã‚µã‚¤ãƒˆã§å®Ÿè¨¼æ¸ˆã¿ã®ã“ã¨ã ãŒã€ãƒ¦ãƒ¼ã‚¶ãƒ¼ã®é–¢ä¸Žã¨ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«ã‚ˆã‚‹å‡¦ç†ãƒ«ãƒ¼ãƒ«ã®ä¸Šæ‰‹ã„ãƒãƒ©ãƒ³ã‚¹ã‚’è¦‹å‡ºã™ã“ã¨ã¯ã€ã¨ã¦ã‚‚é›£ã—ããªã‚ŠãŒã¡ã ã€‚ã“ã‚Œã‹ã‚‰ç´¹ä»‹ã™ã‚‹ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã¯ã€æ‚ªæ„ã®ãªã„ãƒ¦ãƒ¼ã‚¶ãƒ¼ã¨çµã³ã¤ã„ã¦åˆã‚ã¦ã†ã¾ãã„ãã‚‚ã®ã ã€‚ äººæ°—ã‚½ãƒ¼ã‚·ãƒ£ãƒ«
haida 2008/08/20
algorithm

sns
ãƒªãƒ³ã‚¯
Animated Sorting Algorithms
Discussion These pages show 8 different sorting algorithms on 4 different initial conditions. These visualizations are intended to: Show how each algorithm operates. Show that there is no best sorting algorithm. Show the advantages and disadvantages of each algorithm. Show that worse-case asymptotic behavior is not the deciding factor in choosing an algorithm. Show that the initial condition (inp
haida 2008/06/26
algorithm
ãƒªãƒ³ã‚¯
å‰°ä½™ã‚’ä½¿ã‚ãšã«FizzBuzz - ãŠã³ãªãŸã‚“â˜†
æ–°å¹¹ç·šã®ä¸ã®æš‡ãªæ™‚é–“ã‚’ä½¿ã£ã¦ã‚„ã£ã¦ã¿ãŸã€‚ ã¾ãšå‰°ä½™ã‚’ä½¿ã‚ãªã„æ–¹æ³• æœ€å°å…¬å€æ•°ã¨æ•´æ•°è«–ã«ãŠã‘ã‚‹æ³•ã®ç†å±ˆãŒåˆ†ã‹ã£ã¦ã„ã‚Œã°ã€3ã¨5ã®æœ€å°å…¬å€æ•°ã®15å‘¨æœŸã§"Fizz", "Buzz", "FizzBuzz"ã®ãƒ‘ã‚¿ãƒ¼ãƒ³ãŒç¾ã‚Œã‚‹ã“ã¨ãŒã™ãã«ç†è§£ã§ãã‚‹ã€‚ã¡ã†ã“ã¨ã§ã€"Fizz", "Buzz", "FizzBuzz", NULLã‹ã‚‰ãªã‚‹ã®é•·ã•15ã®é…åˆ—ã‚’ç”¨æ„ã—ã¦ã€ãã‚Œã‚’é †ç¹°ã‚Šã«ãƒˆãƒ¬ãƒ¼ã‚¹ã—ã¦è¡Œã‘ã°ã„ã„ã€‚ const char* fizz = "Fizz"; const char* buzz = "Buzz"; const char* fibuzz = "FizzBuzz"; const char* fb[] = { NULL, NULL, fizz, NULL, buzz, fizz, NULL, NULL, fizz, buzz, NULL, fizz, NULL, NULL, fibuzz, };
haida 2008/03/13
cool!!

algorithm
ãƒªãƒ³ã‚¯
The Old techInterview Forum - Shlemiel the Painter
haida 2008/02/22
algorithm
ãƒªãƒ³ã‚¯
ã‚ãªãŸãŒä¸€ç•ªå¥½ããªã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’æ•™ãˆã¦ãã ã•ã„ã€‚ ã¾ãŸã€ãã®ç†ç”±ã‚„ã©ã‚“ãªç‚¹ãŒå¥½ããªã®ã‹ã‚‚æ•™ãˆã¦ãã ã•ã„ã€‚ - äººåŠ›æ¤œç´¢ã¯ã¦ãª
ã‚ãªãŸãŒä¸€ç•ªå¥½ããªã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’æ•™ãˆã¦ãã ã•ã„ã€‚ ã¾ãŸã€ãã®ç†ç”±ã‚„ã©ã‚“ãªç‚¹ãŒå¥½ããªã®ã‹ã‚‚æ•™ãˆã¦ãã ã•ã„ã€‚
haida 2007/11/26
algorithm

programming
ãƒªãƒ³ã‚¯
Google Research Publication: MapReduce: Simplified Data Processing on Large Clusters
MapReduce:Â SimplifiedÂ DataÂ ProcessingÂ onÂ LargeÂ Clusters Jeffrey Dean and Sanjay Ghemawat Abstract MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with t
haida 2007/08/10
google

algorithm
ãƒªãƒ³ã‚¯
Google ã® MapReduce: steps to phantasien t(2005-11-16)
2005-11-16 è¿‘æ³ æƒ…å ±å‡¦ç†å¦ä¼šèªŒã« "skeletal paralell programming" ã¨ã„ã†ã®ãŒè¼‰ã£ã¦ã„ã¦, ã‚ˆãèªã‚“ã§ã„ãªã„ã®ã ã‘ã‚Œã©è¦ã¯ä¸¦åˆ—è¨ˆç®—ã®ãŸã‚ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’ ä½œã‚Šã¾ã—ã‚‡ã†ã¨ã„ã†è©±ã®ã‚ˆã†ã ã£ãŸ. MPI ã‚’åœŸå°ã«ã—ã¦ã„ã‚‹ã‚‰ã—ã„. Google ã® MapReduce ãŒå¼•ç”¨ã•ã‚Œã¦ãŠã‚Š, ãã‚“ãªã‚‚ã®ãŒã‚ã£ãŸã¨æ€ã„ã ã™. èªã‚“ã§ãªã‹ã£ãŸ. ãã†ã„ãˆã°å‰å›žã® ACM Queue ã‚‚ä¸¦åˆ—è¨ˆç®—ã¨ CMP ã®ç‰¹é›†. ã©ã†ã‚‚ä¸¦åˆ—è¨ˆç®—ãŒåŸºæœ¬æŠ€èƒ½ã¨ã—ã¦è¦æ±‚ã•ã‚Œã‚‹æ—¥ã¯æ€ã£ãŸã‚ˆã‚Šè¿‘ã„æ°—ãŒã—ã¦ããŸ. å‹‰å¼·ã—ã¦ãŠã„ãŸæ–¹ãŒã‚ˆã•ãã†ã . æ‰‹å§‹ã‚ã« "MapReduce: Simplified Data Processing on Large Clusters" ã‚’èªã‚“ã§ã¿ãŸ. MapReduce ã¨ã„ã†ã®ã¯ä¸¦åˆ—è¨ˆç®—ã®ãŸã‚ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã§, Google ã®ã‚¤ãƒ³ãƒ•ãƒ©(ãƒ•ã‚¡ã‚¤ãƒ«ã‚·ã‚¹ãƒ†ãƒ ã‚„ã‚¯ãƒ©
haida 2007/08/10
google

algorithm

search
ãƒªãƒ³ã‚¯
VIPS: a Vision-based Page Segmentation Algorithm :: SEM R
ç±³MicrosoftãŒé–‹ç™ºä¸ã®æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã«æè¼‰ã•ã‚Œã‚‹ã¨æ€ã‚ã‚Œã‚‹æ¤œç´¢ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«é–¢ã™ã‚‹è«–æ–‡ãŒåŒç¤¾ã‚µã‚¤ãƒˆ"Microsoft Research"ã«ã¦å…¬é–‹ã•ã‚Œã¦ã„ã‚‹ã€‚é¡Œåã¯ã€ŽVIPS: a Vision-based Page Segmentation Algorithmã€ã€‚ã“ã“ã§ç´¹ä»‹ã•ã‚Œã¦ã„ã‚‹"Vision-based Page Segmentation Algorithm"ï¼ˆä»¥ä¸‹ã€VIPS)ã«Microsoftã¯åŠ›ã‚’å…¥ã‚Œã¦ã„ã‚‹ã‚ˆã†ã ã€‚ ã¾ãšVIPSã«é–¢ã™ã‚‹æ¦‚è¦ã‹ã‚‰ã€‚ A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extr
haida 2007/08/08
search

algorithm

microsoft
ãƒªãƒ³ã‚¯
1