1. 12 March 1999Dip HI KBS Module1 Machine Learning Lucy Hederman

2. 12 March 1999Dip HI KBS Module2 KBS Development Stage 1: analysis of the problem that produces a representation of the problem that can be manipulated by the reasoning system - this representation is often a set of attribute values. Stage 2: developing the reasoning mechanism that manipulates the problem representation to produce a solution.

3. 12 March 1999Dip HI KBS Module3 Stage 2 Knowledge engineering - manually –rule development for a rule-based ES Learning - similarity-based –generalise from examples (training data) Learning - explanation-based –build on prior knowledge –use small number of canonical examples –incorporate explanations, analogy,...

4. 12 March 1999Dip HI KBS Module4 Risk Assessment Example Expert might develop rules like –if collateral is adequate and credit history is good then risk is low. Alternatively build a system which learns from existing data on loan application decisions (see attached). –Similarity-based learning

5. 12 March 1999Dip HI KBS Module5 Classifying apples and pears To what class does this belong?

6. 12 March 1999Dip HI KBS Module6 Supervised Learning Supervised learning –training data classified already Unsupervised learning –acquire useful(?) knowledge without correctly classified training data –category formation –scientific discovery We look at supervised learning only.

7. 12 March 1999Dip HI KBS Module7 Learnability Induction depends on there being useful generalisations possible in the representation language used. Learnability of concepts in a representation language is the ability to express the concept concisely. Random classifications are not learnable.

8. 12 March 1999Dip HI KBS Module8 Similarity-based learning Decision tree (rule) induction –induce a decision tree (set of rules) from the training data. k-nearest neighbour classification –classify a new problem based on the k most similar cases in the training data. Artificial Neural Networks –adjust weights in an NN to reduce errors on training data.

9. 12 March 1999Dip HI KBS Module9 Decision Tree Induction Aim to induce a tree which –correctly classifies all training data –will correctly classify unseen cases ID3 algorithm assumes that the simplest tree that covers all the training examples is the best at unseen problems. –Leaving out extraneous tests should be good for generalising.

10. 12 March 1999Dip HI KBS Module10 ID3 Top-down construction –add selected tests under nodes –each test further partitions the samples –continue till each partition is homogeneous Information-theoretic test selection –maximise information gain ID3 works surprisingly well. Variations and alternatives exist.

11. 12 March 1999Dip HI KBS Module11 k-Nearest Neighbour Classification Data base of previously classified cases kept throughout. Category of target case decided by category of its k nearest neighbours. No inducing or training of a model. “Lazy” learning –work deferred to runtime –compare with neural networks - eager learners

12. 12 March 1999Dip HI KBS Module12 “Nearest” - distance/similarity For query q and training set X (described by features F) compute d(x,q) for each x  X, where and where

13. 12 March 1999Dip HI KBS Module13 k-NN and Noise 1-NN easy to implement –susceptible to noise a misclassification every time a noisy pattern retrieved k-NN with k  3 will overcome this Either –straight voting between the k examples or –weighted votes depending on “nearness” of each example.

14. 12 March 1999Dip HI KBS Module14 K-NN vs. Decision Trees Decision trees test features serially. –If two cases don’t match on first feature tried they don’t match at all. K-NN considers all features in parallel. For some tasks serial testing is OK, for others it’s not.

15. 12 March 1999Dip HI KBS Module15 Dimension reduction in k-NN Not all features required –noisy features a hindrance Some examples redundant –retrieval time depends on no. of examples p features q best features n covering examples m examples

16. 12 March 1999Dip HI KBS Module16 Condensed NN 100 examples 2 categories Different CNN solutions

17. 12 March 1999Dip HI KBS Module17 Feature weighting Feature weights –modify the effect of large continuous distance values –allow some features to be treated as more important than others pull cases with important features in common closer together.

18. 12 March 1999Dip HI KBS Module18 Feature weighting Introspective learning - Test training data on itself –For a correct retrieval increase weight of matching features (pull) decrease weight of un-matching features (pull) –For an incorrect retrieval decrease weight of matching features (push) increase weight of un-matching features (push) Pull Push

19. 12 March 1999Dip HI KBS Module19 (Artificial) Neural Networks Decision tree induction builds a symbolic “causal” model from training data. k-NN builds no model. A neural network is a sub-symbolic, non- causal, distributed, “black box”, model built from training data. ANN output is continuous whereas a k-NN classifies into discrete classes.

20. 12 March 1999Dip HI KBS Module20 NN Prediction of Malignancy A. Tailor and co. paper describes a neural network which computes a probability of malignancy from age, morphological features, and sonographic data. Describes design and testing of the NN. Note intro to NNs in the Appendix

21. 12 March 1999Dip HI KBS Module21 ANN Advantages Particularly suited to pattern recognition –character, speech, image Suited to domains where there is no domain theory or model. Robust - Handle noisy and incomplete data well. Potentially fast. Parallel processing. Flexible and easy to maintain.

22. 12 March 1999Dip HI KBS Module22 ANN Problems Lack explanation Currently implemented in software mostly. Training times can be tedious. Need lots of training and test data. –True of similarity-based learning in general.

23. 12 March 1999Dip HI KBS Module23 ANN Processing Element (PE) Summation - gives PE’s activation level Transfer function - modifies the activation level to produce a reasonable output value (e.g. 0-1).

24. 12 March 1999Dip HI KBS Module24 Typical ANN Structure PE Input layer Hidden layer Output layer There may be –additional hidden layers. –different topologies –different connectivity Choosing ANN structure –is based on problem and –requires some expertise. PE

25. 12 March 1999Dip HI KBS Module25 Learning/Training Aim to obtain desired outputs for each training example. Backpropagation is the most popular learning algorithm. –Initialise all weights associated with inputs to each PE. –Present sample inputs to ANN. –Compare ANN outputs with desired output. –Alter weights to reduce the mean square error, and repeat. until the error is within some tolerance.

26. 12 March 1999Dip HI KBS Module26 Overfitting Training time Error In-sample error Generalisation error Too much training will result in a (k-NN or ANN) model that makes minimal errors on the training data (memorises), but no longer generalises well. Beware.

27. 12 March 1999Dip HI KBS Module27 ANN Development Collect data Separate into training and test sets Define a network structure Select a learning algorithm Set parameters, values, weights Transform data to network inputs Start training, revise weights Stop and test Use the network for new cases. Get more better data Reseparate Redefine structure Select another algorithm Reset