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set of 100 examples, it classifies 83 correctly What is the standard deviation and the 95% confidence interval for the true error rate for Errorv(h) Suppose hypothesis h commits r = 10 errors over a sample of n = 65 independently drawn examples What is the 90% confidence interval (two-sided) for the true error rate What is the 95% one-sided interval (ie, what is the upper bound U such that errorv(h) 5 U with 95% confidence) What is the 90% one-sided interval You are about to test a hypothesis h whose errorV(h) is known to be in the range between 02 and 06 What is the minimum number of examples you must collect to assure that the width of the two-sided 95% confidence interval will be smaller than 01 Give general expressions for the upper and lower one-sided N% confidence intervals for the difference in errors between two hypotheses tested on different samples of data Hint: Modify the expression given in Section 55 Explain why the confidence interval estimate given in Equation (517) applies to estimating the quantity in Equation (516), and not the quantity in Equation (514)

The central components are Xcode and Interface Builder. Beyond this is a range of analysis and performance-management tools and technologies. There is also a variety of supporting utilities to help with refinement, usability, accessibility, and distribution of applications.

Billingsley, P, Croft, D J, Huntsberger, D V, & Watson, C J (1986) Statistical inference for management and economics Boston: Allyn and Bacon, Inc Casella, G, & Berger, R L (1990) Statistical inference Pacific Grove, CA: Wadsworth and BrooksICole DeGroot, M H (1986) Probability and statistics (2d ed) Reading, MA: Addison Wesley Dietterich, T G (1996) Proper statistical tests for comparing supervised classiJication learning algorithms (Technical Report) Department of Computer Science, Oregon State University, Cowallis, OR Dietterich, T G, & Kong, E B (1995) Machine learning bias, statistical bias, and statistical variance of decision tree algorithms (Technical Report) Department of Computer Science, Oregon State University, Cowallis, OR Duda, R, & Hart, P (1973) Pattern classiJicationand scene analysis New York: John Wiley & Sons Efron, B, & Tibshirani, R (1991) Statistical data analysis in the computer age Science, 253, 390395 Etzioni, O, & Etzioni, R (1994) Statistical methods for analyzing speedup learning experiments Machine Learning, 14, 333-347

Geman, S, Bienenstock, E, & Doursat, R (1992) Neural networks and the biadvariance dilemma Neural Computation, 4, 1-58 Gordon, G, & Segre, AM (1996) Nonpararnetric statistical methods for experimental evaluations of speedup learning Proceedings of the ThirteenthInternational Conference on Machine Leaming, Bari, Italy Maisel, L (1971) Probability, statistics, and random processes Simon and Schuster Tech Outlines New York: Simon and Schuster Segre, A, Elkan, C, & Russell, A (1991) A critical look at experimental evaluations of EBL Machine Learning, 6(2) Segre, AM, Gordon G, & Elkan, C P (1996) Exploratory analysis of speedup learning data using expectation maximization Artificial Intelligence, 85, 301-3 19 Speigel, M R (1991) Theory and problems of probability and statistics Schaum's Outline Series New York: McGraw Hill Thompson, ML, & Zucchini, W (1989) On the statistical analysis of ROC curves Statistics in Medicine, 8, 1277-1290 White, A P, & Liu, W Z (1994) Bias in information-based measures in decision tree induction Machine Learning, 15, 321-329

Bayesian reasoning provides a probabilistic approach to inference It is based on the assumption that the quantities of interest are governed by probability distributions and that optimal decisions can be made by reasoning about these probabilities together with observed data It is important to machine learning because it provides a quantitative approach to weighing the evidence supporting alternative hypotheses Bayesian reasoning provides the basis for learning algorithms that directly manipulate probabilities, as well as a framework for analyzing the operation of other algorithms that do not explicitly manipulate probabilities

Here is a brief introduction to each of the main Xcode-focused topics that I ll be covering in the book.

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