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(b) You now draw a new set of 100 instances, drawn independently according to the same distribution DYou find that h misclassifies 30 of these 100 new examples Can you give an interval into which this true error will fall with approximately 95% probability (Ignore the performance over the earlier training data for this part) If so, state it and justify it briefly If not, explain the difficulty (c) It may seem a bit odd that h misclassifies 30%of the new examples even though it perfectly classified the training examples Is this event more likely for large n or small n Justify your answer in a sentence

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EXPERIENCE Director, City of Beaufort, Women s Resource Center, Beaufort, SC Manage Center that provides family, personal, and career counseling, as well as public health services to professional women; supervise staff of eight including a clinical psychologist, financial planner, psychiatric social worker, and philosopher; manage budget of $400,000; generate 50% of budget from consultant services to government agencies and businesses 1992 present

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Angluin, D (1992) Computational learning theory: Survey and selected bibliography Proceedings of the Twenty-FourthAnnual ACM Symposium on Theory of Computing (pp 351-369) ACM Press Angluin, D, Frazier, M, & Pitt, L (1992) Learning conjunctions of horn clauses Machine Learning, 9, 147-164 Anthony, M, & Biggs, N (1992) Computational learning theory: An introduction Cambridge, England: Cambridge University Press Blumer, A, Ehrenfeucht, A, Haussler, D, & Warmuth, M (1989) Learnability and the VapnikChemonenkis dimension Journal of the ACM, 36(4) (October), 929-965 Ehrenfeucht, A, Haussler, D, Kearns, M, & Valiant, L (1989) A general lower bound on the number of examples needed for learning Informution and computation, 82, 247-261 Gold, E M (1967) Language identification in the limit Information and Control, 10, 447-474 Goldman, S (Ed) (1995) Special issue on computational learning theory Machine Learning, 18(2/3), February Haussler, D (1988) Quantifying inductive bias: A1 learning algorithms and Valiant's learning framework ArtGcial Intelligence, 36, 177-221 Kearns, M J, & Vazirani, U V (1994)An introductionto computational learning theory Cambridge, MA: MIT Press Laird, P (1988) Learning from good and bad data Dordrecht: Kluwer Academic Publishers Li, M, & Valiant, L G (Eds) (1994) Special issue on computational learning theory Machine Learning, 14(1) Littlestone, N (1987) Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm Machine Learning, 2, 285-318 Littlestone, N, & Warmuth, M (1991) The weighted majority algorithm (Technical report UCSCCRL-91-28)Univ of California Santa Cruz, Computer Engineering and Information Sciences Dept, Santa Cruz, CA Littlestone, N, & Warmuth, M (1994) The weighted majority algorithm Information and Computation (log), 212-261 Pltt, L (Ed) (1990) Special issue on computational learning theory Machine Learning, 5(2) Natarajan, B K (1991) Machine learning: A theoretical approach San Mateo, CA: Morgan Kaufmann Valiant, L (1984) A theory of the learnable Communications of the ACM, 27(1 I), 1134-1 142 Vapnik, V N (1982) Estimation of dependences based on empirical data New York: SpringerVerlag Vapnik, V N, & Chervonenkis, A (1971) On the uniform convergence of relative frequencies of events to their probabilities Theory of Probability and Its Applications, 16, 264-280.

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P(a, = wkJvj)for all i, j, k, m Therefore, we estimate the entire set of probabilities P(a1 = w lvj), P(a2 = w lv,) by the single position-independent probk k ability P(wklvj), which we will use regardless of the word position The net effect is that we now require only 250,000 distinct terms of the form P(wklvj) This is still a large number, but manageable Notice in cases where training data is limited, the primary advantage of making this assumption is that it increases the number of examples available to estimate each of the required probabilities, thereby increasing the reliability of the estimates To complete the design of our learning algorithm, we must still choose a method for estimating the probability terms We adopt the m-estimate-Equation (622)-with uniform priors and with rn equal to the size of the word vocabulary Thus, the estimate for P(wklvj) will be

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