Lesson plan
Version space learning
Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction : H_1 \lor H_2 \lor ... \lor H_n (i.e., one or more of hypotheses 1 through are true). A version space learning algorithm is presented with examples, which it will use to restrict its hypothesis space; for each example , the hypotheses that are inconsistent with are removed from the space. This iterative refining of the hypothesis space is called the candidate elimination algorithm, the hypothesis space maintained inside the algorithm, its version space.
Article summary
Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction : H_1 \lor H_2 \lor ... \lor H_n (i.e., one or more of hypotheses 1 through are true). A version space learning algorithm is presented with examples, which it will use to restrict its hypothesis space; for each example , the hypotheses that are inconsistent with are removed from the space. This iterative refining of the hypothesis space is called the candidate elimination algorithm, the hypothesis space maintained inside the algorithm, its version space.
Key claims
No claims recorded.
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
No sources recorded.
Key claims and bibliography are generated from the same tracked claims and sources as the article page.