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SNoW: Sparse Network of Winnows

SNoW: Sparse Network of Winnows. Presented by Nick Rizzolo. Introduction. Multi-class classification Infinite attribute domain User configurable linear threshold unit networks Variety of update rules, algorithmic extensions. Outline. The Basic System Training Algorithmic Extensions

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SNoW: Sparse Network of Winnows

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  1. SNoW:Sparse Network of Winnows Presented by Nick Rizzolo Cognitive Computations Software Tutorial

  2. Introduction • Multi-class classification • Infinite attribute domain • User configurable linear threshold unit networks • Variety of update rules, algorithmic extensions Cognitive Computations Software Tutorial

  3. Outline • The Basic System • Training • Algorithmic Extensions • Testing • Tuning Cognitive Computations Software Tutorial

  4. The Basic System Targets (concepts) Weighted edges, instead of weight vectors Features • SNoW only represents the targets and weighted edges • Prediction is “one vs. all” Cognitive Computations Software Tutorial

  5. activation threshold Update Rules • Winnow – mistake driven • Promotion: if , • Demotion: if , • Perceptron – mistake driven • Promotion: if , • Demotion: if , • Naïve Bayes – statistical Cognitive Computations Software Tutorial

  6. snow –train –I learnMe.snow –F learnMe.net –W 2,0.5,3.5,1:1-3 –z + -s s snow –test –I testMe.snow -F learnMe.net –o winners Command Line Interface snow –train –I <example file> -F <network file> -W <α>,<β>,<θ>,<initial weight>:<targets> -P <α>,<θ>,<initial weight>:<targets> snow –test –I <example file> -F <network file> … Cognitive Computations Software Tutorial

  7. 2, 1, 2, 1 2, 1, 1, 2 1, 1 = 4 1, 1, 1 = 2 2, 1, 2, 1 2, 2 1, 1 = 1 1, 1001, 1006: 1 2 3 2, 1002, 1007, 1008: 1, 1004, 1007: 3, 1006, 1004: 3, 1004, 1005, 1009: 1001 1002 1003 1004 1005 1006 1007 1008 1009 A Training Example 2, 2, 2, 2 2, 2 2, 2, 2 1001, 1005, 1007: Update rule: Winnow α = 2, β = ½,θ = 3.5 Cognitive Computations Software Tutorial

  8. Eligibility -e count:1 • Discarding -d abs:0.1 • “Fixed feature” -f - • Feature conjunctions -g - • Prediction threshold -p 0.5 • Smoothing -b 5 Bells & Whistles Cognitive Computations Software Tutorial

  9. Outline • The Basic System • Training • Algorithmic Extensions • Testing • Tuning Cognitive Computations Software Tutorial

  10. Clouds (Voting) • Multiple target nodes per concept 1 2 3 • Learning still independent • Cloud activation is weighted sum of its targets’ activations • Decreasing function of mistakes gives those weights snow –train –I learnMe.snow –F learnMe.net –W :1-3 –P :1-3 Cognitive Computations Software Tutorial

  11. 1, 1001, 1005, 1007; 1, 3: 1001, 1005, 1007; 1, 3: The Sequential Model • Larger confusion set → lower prediction accuracy • Put prior knowledge in each example Cognitive Computations Software Tutorial

  12. The Thick Separator -S <p>,<n> • Theory is similar to SVM • Increases “margin” • Improves generalization • Almost always improves performance Cognitive Computations Software Tutorial

  13. Constraint Classification -O <+|-> • Promotion / demotion depends on activation comparisons • More expressive than independent update rules • Sometimes difficult to realize performance improvements Cognitive Computations Software Tutorial

  14. Other Training Policies • Regression • Function approximation • (Exponentiated) Gradient Descent • Threshold relative updating • Each update moves hyperplane to example • Single target training and testing -G <+|-> -t <+|-> Cognitive Computations Software Tutorial

  15. Outline • The Basic System • Training • Algorithmic Extensions • Testing • Tuning Cognitive Computations Software Tutorial

  16. Winnow / Perceptron σ Softmax: θ - Ω Sigmoid Functions • How to compare activations from different algorithms? • Sigmoids map activations to (0:1) Naïve Bayes already yields (0:1) Cognitive Computations Software Tutorial

  17. Output Modes -o <below> • accuracy (the default) • Assumes labeled testing examples • Simply reports SNoW’s performance • winners • softmax • allactivations • Example 47 Label: 0 0: 0.70005 4.8475 0.77582*1: 0.30915 3.1959 0.148762: 0.18493 2.5167 0.075420 Cognitive Computations Software Tutorial

  18. Outline • The Basic System • Training • Algorithmic Extensions • Testing • Tuning Cognitive Computations Software Tutorial

  19. The major players: -W <α>,<β>,<θ>,<initial weight>:<targets> -P <α>,<θ>,<initial weight>:<targets> -S <thickness> Tuning • Parameter settings make a big difference • Want to pick the right algorithm • Don’t want to over-fit • Trial & error -W <α>,<β>,<θ>,<initial weight>:<targets> -P <α>,<θ>,<initial weight>:<targets> -S <thickness> -r <rounds> -u <+ | -> tune.pl –train <file> -arch W:<targets> … Cognitive Computations Software Tutorial

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