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Chapter 10 Machine Learning: Symbol-Based

Chapter 10 Machine Learning: Symbol-Based. Contents. A Framework Version Space Search ID3: Decision Tree. Machine Learning. AI systems grow from a minimal amount of knowledge by learning Herbert Simon (1983):

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Chapter 10 Machine Learning: Symbol-Based

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  1. Chapter 10 Machine Learning: Symbol-Based Contents • A Framework • Version Space Search • ID3: Decision Tree Artificial Intelligence

  2. Machine Learning • AI systems grow from a minimal amount of knowledge by learning • Herbert Simon (1983): • Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population • Machine learning issues: • Generalization from experience • Induction • Inductive biases • Performance change: improve or degrade Artificial Intelligence

  3. Machine Learning Categories • Symbol-based learning • Inductive learning -- learning by examples • Supervised learning/unsupervised learning • Concept learning –- classification • Concept formation -- clustering • Explanation-based learning • Reinforcement learning • Neural/connectionist networks • Genetic/evolutionary learning Artificial Intelligence

  4. A general model of the learning process Artificial Intelligence

  5. Learning Components • Data and goals of learning task • What are given – training instances • What are expected • Knowledge representation • Logic expressions • Decision trees • Rules • Operations • Generalization/specialization • Heuristic rules • Weight adjusts • Concept space • Search space: representation, format • Heuristic search • Search control in the concept space Artificial Intelligence

  6. Learning By Examples • Patrick Winston (1975) • Given a set of positive and a set of negative examples • Find a concept representation • Semantic network representation • Example • Learn a general definition of structural concept, say “arch” • Positive examples: examples of arch • What an arch looks like, to define the arch • Negative examples: near misses • What an arch doesn’t look like, to avoid the over-coverage of arch Artificial Intelligence

  7. Examples and near misses for the concept “arch.” Artificial Intelligence

  8. Generalization of descriptions to include multiple examples. Artificial Intelligence

  9. Generalization of descriptions to include multiple examples (cont’d) Artificial Intelligence

  10. Specialization of a description to exclude a near miss. In c we add constraints to a so that it can’t match with b. Artificial Intelligence

  11. Version Space Search • Inductive learning as search through a concept space • Generalization imposes an ordering on the concepts in the space and uses the ordering to guide the search • Generalization • Principles • Extend the coverage of instances • Shorten/shrink the constrains • Operations • Replacing constant with variables • Dropping conditions from a conjunctive expression • Adding a disjunct to an expression • Replacing a concept with one of its parent concepts Artificial Intelligence

  12. A concept space: • Initial state obj(X, Y, Z) might cover all instances: too general • As more instances are added, X, Y, Z will be constrained Artificial Intelligence

  13. Version Space Search Algorithms • Characteristics of these algorithms • Data-driven • Positive examples to generalize the concept • Negative examples to constrain the concept (avoid overgeneralization) • Procedure: • Starting from whole space • Reducing the size of the space as more examples included • Finding regularities (rules) in the training data • Generalization on these regularities (rules) • Three algorithms • Reducing the size of the version space in a specific to general direction • Reducing the size of the version space in a general to specific direction • Combination of above: candidate elimination algorithm Artificial Intelligence

  14. Negative Examples The role of negative examples in preventing overgeneralization by forcing the learner to specialize concepts in order to exclude negative examples Artificial Intelligence

  15. Specific to General Search • Maintains a set S of candidate concepts, the maximally specific generalizations from the training instances • A concept c is maximally specific if it • covers all positive examples, non of the negative examples, and • for any other concept c’ that covers the positive examples, c≤c’ • The algorithm uses • Positive examples to generalize the candidate concepts • Negative example to avoid overgeneralization Artificial Intelligence

  16. Specific to General Search Algorithm For hypothesis set S: Artificial Intelligence

  17. Specific to general search of the version space learning the concept “ball.” Artificial Intelligence

  18. General to Specific Search • Maintains a set G of maximally general concepts • A concept c is maximally general if it • covers non of the negative training examples, and • for any other concept c’ that covers no negative training examples, cc’ • The algorithm uses • negative examples to specialize the candidate concepts • Positive examples to eliminate overspecialization Artificial Intelligence

  19. General to Specific Search Algorithm Artificial Intelligence

  20. General to specific search of the version space learning the concept “ball.” Artificial Intelligence

  21. Candidate Elimination Algorithm • Combination of above two algorithms into a bi-direction search • Maintains two sets of candidate concepts • G, the set of maximally general candidates • S, the set of maximally specific candidates • The algorithm specializes G and generalizes S until they converge on the target concept. Artificial Intelligence

  22. Candidate Elimination Algorithm Artificial Intelligence

  23. The candidate elimination algorithm learning the concept “red ball.” Artificial Intelligence

  24. Converging boundaries of the G and S sets in the candidate elimination algorithm. Artificial Intelligence

  25. Decision Trees • Learning algorithms of inducing concepts from examples • Characteristics • A tree structure to represent the concept, equivalent to a set of rules • Entropy and information gain as heuristics for selecting candidate concepts • Handling noise data • Classification – supervised learning • Typical systems: ID3, C4.5, C5.0 Artificial Intelligence

  26. Data from credit history of loan applications Artificial Intelligence

  27. A decision tree for credit risk assessment. Artificial Intelligence

  28. A simplified decision tree for credit risk assessment. Artificial Intelligence

  29. Decision Tree Construction Algorithm The induction algorithm begins with a sample of correctly classified members of the target categories. Artificial Intelligence

  30. A partially constructed decision tree. Another partially constructed decision tree. Artificial Intelligence

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