Os Ramirez

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  • + chorny chorny 3 years ago
    it would be better to write
    open my $quotefile,’>’, $quotes_filename or die
    as vertical bars (Slide share does not allow this symbol in comments) will check if $quotes_filename is not empty.
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Os Ramirez - Presentation Transcript

  1. Machine Learning  Made Easy with Perl Lino Ramirez ramirez@aranducorp.com
  2. From Wikipedia
  3. From Wikipedia
  4. From Wikipedia
  5. From Wikipedia From Wikipedia
  6. From Wikipedia From Wikipedia
  7. From Wikipedia
  8. $$$
  9. 8 years later ...
  10. A MSc later ...
  11. A PhD later ...
  12. Computers  should “empower”  people not replace them
  13. Key Lesson:  “It's all about  empowering people”
  14. What do you want to accomplish? Machine Learning: 3 phases
  15. Well ... There is this guy ... Machine Learning: 3 phases
  16. Video
  17. Is that helping? Video
  18. Not really! You should ... Video
  19. Machine Learning:
  20. Machine Learning: 3 phases Preparation Phase Modeling Implementation Phase Phase
  21. Machine Learning: Preparation •Definition •Gathering •Analysis Preparation •Cleaning Phase •Selection Modeling Implementation Phase Phase
  22. Machine Learning: Modeling •Selection Preparation •Development •Evaluation Phase Modeling Implementation Phase Phase
  23. Machine Learning: Implementation •Evaluation •Implementation Preparation Phase Modeling Implementation Phase Phase
  24. Case Study
  25. Case Study Implemented 100% in Perl
  26. Problem For a selected group of components for Nasdaq composite, if I cluster the financial quotes into three segments, what are their profiles?
  27. Preparation Phase
  28. Components for Nasdaq composite?
  29. Cluster?
  30. Profiles?
  31. Gathering data
  32. Analysis
  33. Analysis Missing values
  34. Analysis Missing values Ask customer
  35. Analysis Different range of values
  36. Analysis Different range of values Scaling
  37. Modeling Phase
  38. Selection
  39. Clustering Method for dividing data elements into groups so that items in the same group are as similar as possible, and items in different groups are as dissimilar as possible.
  40. Fuzzy C-Means (FCM) Items are allowed to belong to more than one group
  41. Membership to Cluster Centered in (5.80, 5.65 ) 9.5 0.09 9 8.5 0.11 0.01 8 7.5 0.52 0.13 7 6.5 0.98 0.98 0.39 6 5.5 0.94 0.96 5 4.5 4.5 5.5 6.5 7.5 8.5 9.5
  42. Membership to Cluster Centered in (5.80, 5.65 ) 9.5 0.09 9 8.5 0.11 0.01 8 7.5 0.52 0.13 7 6.5 0.98 0.98 0.39 6 5.5 0.94 0.96 5 4.5 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5
  43. Clusters' Shape Euclidean distance Manhattan distance Tchebychev distance
  44. FCM: Step by Step Initialize partition matrix stop = false While ( stop is false ) Compute clusters' centers Compute new partition matrix If partition matrices are close to each other stop = true Else partition matrix = new partition matrix
  45. Development
  46. Installing PDL Ubuntu or Debian based distribution $ sudo apt-get install pdl pgplot5 $ sudo apt-get install libterm-readline-gnu-perl $ sudo cpan cpan> install PGPLOT
  47. Installing PDL (cont.) Other Linux distribution or Mac OS X: See: http://wiki.jach.hawaii.edu/pdl_wiki-bin/wiki/SiteMap Go to: Getting Started with PDL and then to: Installing PDL the quick and easy way
  48. Getting Started with PDL Project Homepage: http://pdl.perl.org/ Mailing Lists: http://pdl.perl.org/maillists/index_en.html PDL at PerlMonks: http://perlmonks.org/?node_id=626721
  49. n−1 m uij xj ∑ j=0 vi = ,    0≤i≤c−1 n−1 m uij ∑ j=0
  50. 1 u ij= c −1 2 / m −1  ∑  d ij /d kj  k =0
  51. Evaluation
  52. Implementation Phase
  53. Implementation Phase Ask your customer
  54. Case Study
  55. Case Study Implemented partially in Perl
  56. Problem For patients with scoliosis, Can you design a system that using surface topography could predict internal spinal deformity sufficiently well to replace some radiographs?
  57. Preparation Phase
  58. Scoliosis?
  59. Scoliosis
  60. Surface Topography?
  61. Predict?
  62. Gathering data
  63. Analysis
  64. Additional features?
  65. Analysis Missing values
  66. Analysis Missing values Ask customer
  67. Analysis Different range of values
  68. Analysis Different range of values Scaling
  69. Modeling Phase
  70. Selection
  71. SVM vs. Neural Networks
  72. Neural Networks Classifier
  73. Neural Networks Classifier
  74. Neural Networks Classifier
  75. Neural Networks Classifier
  76. Neural Networks Classifier
  77. Support Vector Machines Classifier
  78. Support Vector Machines Classifier
  79. Development
  80. LIBSVM -- A Library for Support Vector Machines http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Award winning SVM package distributed with a “modified BSD license”
  81. Development SVM configuration
  82. Development SVM configuration Crossvalidation
  83. Evaluation
  84. Evaluation How to test the performance?
  85. Evaluation How to test the performance? Leave one out
  86. Evaluation Results
  87. Evaluation Results 85% determining need of treatment
  88. Evaluation Results 69% 3-class classification
  89. Implementation Phase
  90. Implementation Phase Ask your customer
  91. Conclusion
  92. Conclusion Perl excels at empowering people in all three phases of the development of a machine learning application
  93. Thank you! Open for questions now or later for samples and slides email me at: ramirez@aranducorp.com This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License
  94. Linear Polynomial RBF Sigmoid

+ oscon2007oscon2007, 3 years ago

custom

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