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パターン認識と機械学習入門

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パターン認識と機械学習入門

  1. 1. Contents1. 簡単な自己紹介2. 理論と現実の妥協点探し‐最適化問題と機械学習‐ *パターン認識と機械学習入門* パターン認識と機械学習という 人間の意思決定の基礎となるような 学際的な理論の世界からの眺めを共有します
  2. 2. ••••••
  3. 3. •••••  •••
  4. 4. •         
  5. 5. •• • 
  6. 6. • • • • • •
  7. 7.  o o
  8. 8.  o o o
  9. 9. http://ja.wikipedia.org/wiki/
  10. 10. http://ja.wikipedia.org/wiki/
  11. 11. http://ja.wikipedia.org/wiki/
  12. 12. http://ja.wikipedia.org/wiki/
  13. 13. 
  14. 14. 
  15. 15. つまりあなたが アヒルと白鳥の 雑多な集団の中から白鳥を探し出したいのなら
  16. 16.
  17. 17. •
  18. 18.  o o
  19. 19. http://mathworld.wolfram.com/TravelingSalesmanProblem.html
  20. 20. http://www.isgtw.org/feature/isgtw-feature-traveling-salesman-meets-distributed-computing
  21. 21. http://www.isgtw.org/feature/isgtw-feature-traveling-salesman-meets-distributed-computing
  22. 22. http://mathematica-bits.blogspot.jp/2011/01/listing-all-hamiltonian-cycles-of-graph.html
  23. 23. http://mathematica-bits.blogspot.jp/2011/01/listing-all-hamiltonian-cycles-of-graph.html
  24. 24. http://mathematica-bits.blogspot.jp/2011/01/listing-all-hamiltonian-cycles-of-graph.html
  25. 25. 
  26. 26. 
  27. 27. http://fsharpnews.blogspot.jp/2009/07/traveling-salesman-demo-from-fnet.html
  28. 28. http://fsharpnews.blogspot.jp/2009/07/traveling-salesman-demo-from-fnet.html
  29. 29. http://fsharpnews.blogspot.jp/2009/07/traveling-salesman-demo-from-fnet.html
  30. 30. http://fsharpnews.blogspot.jp/2009/07/traveling-salesman-demo-from-fnet.html
  31. 31. 混沌としているようで もしかしたらコスト関数は 最小化問題に悩むほど複雑怪奇な姿ではないことが 見えてくるかもしれません
  32. 32. それにしても
  33. 33. こんなつまらない話は 「釈迦に説法」かもしれないから 恥ずかしいしやめておこうと思いとどまっても良さそうなものですが
  34. 34.
  35. 35. Enjoy Uncertainty !
  36. 36. Reference"Pattern Recognition and Machine Learning"Christopher M. BishopSpringer; 1st ed. 2006. Corr. 2nd printing edition (October 1,2007)"Truth and Probability"Frank Plumpton Ramsey (1926)"The physical basis of IMRT and inverse planning"S WebbBritish Journal of Radiology (2003) 76, 678-689
  37. 37. Wikipedia 渡辺慧http://ja.wikipedia.org/wiki/%E6%B8%A1%E8%BE%BA%E6%85%A7『No Free Lunch Theorem—理想の**の探し方—』矢吹太朗 伊庭斉志http://www.unfindable.net/article/no_free_lunch/nfl.pdf『量子アニーリング』大関真之 西森秀稔 (2010)http://www-adsys.sys.i.kyoto-u.ac.jp/mohzeki/QA.pdf『統計的機械学習における量子アニーリング 情報統計力学の最前線情報と揺らぎの制御の物理学を目指して』佐藤一誠ら(2012)http://www.slideshare.net/issei_sato/ss-12125104

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