14722_Adaboost Ensemble Learning

Page 1: Initializing the Adaboost Model

  • Task: Initialize classification error for images A, B, C, D, E, F.

  • Error Calculation:

    • Classification errors for images:

      • A: 0.2

      • B: 0.2

      • C: 0.2

      • D: 0.2

      • E: 0.2

      • F: 0.2

  • Model Output: Combined output = 0.4

Page 2: Model Classification

  • Adaboost Model Configuration:

    • Weight allocation:

      • A: 0.2

      • B: 0.4

      • C, D, E: 0.2 (X indicates involvement)

Page 3: Adjusting Model Parameters

  • Adaboost Model Adjustments:

    • Classification weights:

      • A: 0.2

      • B: 0.4

      • C: 0.4

      • D, E: 0.2 (indicated as X)

Page 4: Re-evaluation of Classification

  • Task: Classify images A, B, C, D.

  • Error Assessment:

    • Error values:

      • A: 0.2

      • B: 0.2

      • C: 0.2

      • D: 0.2

    • Combined output = 0.4

Page 5: Extended Classification

  • Classification Process: For images A, B, C, D, E.

  • Error Measurements:

    • Updated errors:

      • A: 0.4

      • B: 0.2

      • C: 0.2

      • D: 0.4

    • Overall output = 0.4

Page 6: Error Calculation Refinement

  • Adaboost Model Analysis: Classification of images A, B, C, D, E, F.

  • Error Metrics:

    • Errors for each image = 0.2

  • Combined Output of Errors = 0.2

Page 7: Classifier Selection

  • Image Classifier Selection Process:

    • Select classifiers for A, B, C, D, E, F.

    • Weight: 0.4

  • Calculate Adaboost Weight:

    • Weight = 0.2027

Page 8: Weight Normalization

  • Weight Calculation for Classifiers A through F:

    • Normalized weights:

      • A: 0.25

      • B: 0.166

      • C: 0.25

      • D: 0.166

      • E: 0.166

  • Classification error recalculated as:

    • F = 0.2027

Page 9: Final Weight Updates

  • Classifier Selection & Weight Update:

  • Classifier outputs:

    • F = 0.25, 0.166, 0.25, 0.166, 0.166

  • Updated Classifier Weights:

    • Model reviewed:

      • F = 0.33, 0.3465, 0.2027

Page 10: Repeating Process

  • Adaboost Model Workflow: Repeat processes as needed.

  • Classification for images A, B, C, D, E, F under various weights.

    • Series of values illustrating weights:

      • 0.2027, 0.12, 0.25, 0.18, ...

Page 11: Accuracy Improvement

  • Classification Accuracy Achievement:

  • Classification output for items A, B, C, D, E, F:

    • F = 0.26

    • Weights updated for A = 0.2027, D = 0.3465

  • Result: Higher classification accuracy shown across the model.