1 / 35

Concept Learnin g

Concept Learnin g. C.C. Fang Reference: Mitchell Machine Learning, Chapter 2. Outline. Concept learning Learning from instances (examples) General-to-specific ordering over hypotheses Version spaces and candidate-elimination algorithm The role of bias. The definition of Concept Learning.

myra
Télécharger la présentation

Concept Learnin g

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Concept Learning C.C. Fang Reference: Mitchell Machine Learning, Chapter 2

  2. Outline • Concept learning • Learning from instances (examples) • General-to-specific ordering over hypotheses • Version spaces and candidate-elimination algorithm • The role of bias

  3. The definition of Concept Learning • Inferring Boolean-valued functions from training examples; • Concept learning is learning a function which has a Boolean-valued output • f: X → {0,1} • Many machine learning approaches us this simplistic binary view of the world • Example: What is the general concept?

  4. Representing Hypotheses Many possible representations... Here, h is conjunction of constraints on attributes. Each constraint can be • a specfic value (e.g., “Water = Warm”) • don’t care (e.g., “Water =?”) • no value allowed (e.g.,“Water=ψ ;”)

  5. A Concept Learning Task

  6. The Inductive Learning Hypothesis • Note: the only information available about c is c(x) for each <x, c(x)> in D. • Any hypothesis found to approximate the target function well over a sufficiently large set of training examples will also approximate the target function well over other observed example. • Assumption of Inductive Learning • • The best hypothesis regarding the unseen instances is thehypothesis that best fits the observed training data

  7. Viewing Learning As a Search Problem (1/2)

  8. Viewing Learning As a Search Problem (2/2) • Study of learning algorithms that examine different strategies for searching the hypothesis space, e.g., • Find-S Algorithm • List-Then-Eliminate Algorithm • Candidate Elimination Algorithm • • How to exploit the naturally occurring structure in the hypothesis space ? • Relations among hypotheses , e.g., • General-to-Specific-Ordering

  9. General-to-Specific-Ordering of Hypothesis (1/3)

  10. General-to-Specific-Ordering of Hypothesis (2/3)

  11. General-to-Specific-Ordering of Hypothesis (3/3)

  12. Find-S Algorithm (1/3)

  13. Find-S Algorithm (2/3)

  14. Find-S Algorithm (3/3)

  15. Complaints about Find-S • Can not tell whether it has learned concept (Output only one. Many other consistent hypotheses may exist!) • Picks a maximally specific h (why?) (Find a most specific hypothesis consistent with the training data) • Can not tell when training data inconsistent – What if there are noises or errors contained in training examples • Depending on H, there might be several !

  16. Consistence of Hypotheses

  17. Version Space

  18. List-Then-Eliminate Algorithm 1.VersionSpace ← a list containing all hypotheses in H 2.For each training example, <x, c(x)> remove from VersionSpace any hypothesis h for which h(x)≠c(x) – i.e., eliminate hypotheses inconsistent with any training examples – The VersionSpace shrinks as more examples are observed 3.Output the list of hypotheses in VersionSpace

  19. Drawbacks of List-Then-Eliminate • The algorithm requires exhaustively enumerating all hypotheses in H • An unrealistic approach ! (full search) • If insufficient (training) data is available, the algorithm will output a huge set of hypotheses consistent with the observed data

  20. Example Version Space

  21. Representing Version Space

  22. Candidate Elimination Algorithm (1/3)

  23. Candidate Elimination Algorithm (2/3)

  24. Candidate Elimination Algorithm (3/3)

  25. Example Trace (1/5)

  26. Example Trace (2/5)

  27. Example Trace (3/5)

  28. Example Trace (4/5)

  29. Example Trace (5/5)

  30. Overview Example Trace

  31. What Next Training Example

  32. How Should These Be Classified ?

  33. Ranking Inductive Learners according to their Biases Weak • Rote-Learner: This system simply memorizes the training data and their classification--- No generalization is involved. • Candidate-Elimination: New instances are classified only if all the hypotheses in the version space agree on the classification • Find-S: New instances are classified using the most specific hypothesis consistent with the training data Bias Strength Strong

  34. Biased Hypothesis Space

  35. Summary Points • Concept learning as search through H • General-to-specific ordering over H • Version space candidate elimination algorithm –S and G boundaries characterize learners uncertainty • Learner can generate useful queries

More Related