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Ada boost
1. The document describes a general boosting procedure for combining weak learners to create a strong learner. 2. It involves initializing the model, learning weak learners, calculating error rates, adjusting the distribution of the training data, and combining weak learners. 3. It also describes the AdaBoost algorithm which implements this general boosting procedure and learns weak learners in sequence while focusing more on examples that previous learners got wrong.
Ada boost
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- 7. ⼀一 ⽅方 ⾏行行 ⾼高 ⽣生
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- 9. ⼀一 ⽣生 A General Boosting Procedure 1. Initialize 2. From t = 1 to T. (T: ) - : Learn a weak learner - : Calculate error - : Create new distribution 3. : Combine weak learners
- 10. ⼀一 ⽣生 A General Boosting Procedure 1. Initialize 2. From t = 1 to T. (T: ) - : Learn a weak learner - : Calculate error - : Create new distribution 3. : Combine weak learners
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- 18. AdaBoost Algorithm Input: 1. : Calculate initial distribution 2. From t = 1 to T. - , :Learn and Calculate Error - if then break - Calculate Weight and next distribution Outputs:
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- 21. A General BoostingProcedure 1. Initialize 2. From t = 1 to T. (T: ) - : Learn a weak learner - : Calculate error - : Create new distribution 3. : Combine weak learners ⽅方 ⼩小
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- 23. AdaBoost Algorithm Input: 1. : Calculate initial distribution 2. From t = 1 to T. - , :Learn and Calculate Error - if then break - Calculate Weight and next distribution Outputs:
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