Description
1. Rocchio’s algorithm (25pt) a. In Rocchio’s algorithm, what weight setting for α/β/γ does a “Find pages like this one” search correspond to? b. Under what conditions would the modified query qm in In Rocchio’s algorithm be the same as the original query q0 ? In all other cases, is qm closer than q0 to the centroid of the relevant documents? 2. Relevance feedback (25pt) a. Give three reasons why relevance feedback has been little used in web search. b. Why is positive feedback likely to be more useful than negative feedback to an IR system? Why might only using one non-relevant document be more effective than using several?ta 3. Boosting (25pt) What’s the relation between Adaboost and Boosting? What’s the difference between Adaboost and Gradient Boosting? Please explain briefly. 4. Adaboost (25pt) Below is a 2-classify training set. Sample 1 2 3 4 5 6 7 8 9 10 X 0 1 2 3 4 5 6 7 8 9 Y 1 1 1 -1 -1 -1 1 1 1 -1 Assume we have a weak classifier G. G uses a threshold v to predict Y, which is generated by xv. G uses the v that minimize the error rate for the classification. Using the definition and equations in the slides, show how you get a strong classifier by Adaboost.
