中文XML论坛--求助各位大哥大姐

贴子主题：求助各位大哥大姐

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小弟我初学机器学习，有很多问题不懂，希望大家帮忙回答一下以下几个问题。
1. In which sense can computers "learn"? Describe the framework of concept learning.

2. Give some own example(s) of a learning problem (where we are in need of some results, but it is infeasible to obtain them in a direct way).

3. What is "generalization" beyond observed data, and has it something to do with the notion of a "more general hypothesis"?

4. Is it necessary to restrict the hypothesis space H, and how can (or should) it be done practically? Relate H to the notion of "inductive bias".

5. How is it possible at all for learning algorithms to make predictions about unseen data? To what extent can we trust their results?

6. Which computational difficulties can arise in concept learning tasks? How do various algorithms cope with these difficulties?

7. Does the ability to choose own training examples add power to a learner?

8. What is the general scheme of decision tree learning (apart from the details of ID3)?

9. What is the origin of overfitting, why is it fatal for learning, and what can be done to avoid it?


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