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Make each control 280 pixels wide and center the text. For the UITextField, make the Placeholder attribute Write message here (the placeholder is text that is shown in a grayedout form in a Text Field as a hint to the user). Make the window background pale gray (set the Brightness value in the Gray Scale Colors panel to 90%). You may be wondering why all of the controls are in the top half of the window. Run the emulator ( R) to see why (Figure 4 6): click in the Text Field and you will see the virtual keyboard come up. Your user interface design should take account of the fact that the keyboard may need to be accommodated on the screen. We ll be looking further at this keyboard shortly. Quit from the emulator ( Q).

An alternative learning model, called the mistake bound model, is used to analyze the number of training examples a learner will misclassify before it exactly learns the target concept For example, the HALVING algorithm will make at most Llog, 1 H 1J mistakes before exactly learning any target concept drawn from H For an arbitrary concept class C , the best worstcase algorithm will make Opt (C) mistakes, where log,(lCI) VC(C> 5 Opt(C) I The WEIGHTED-MAJORITY algorithm combines the weighted votes of multiple prediction algorithms to classify new instances It learns weights for each of these prediction algorithms based on errors made over a sequence of examples Interestingly, the number of mistakes made by WEIGHTED-MAJORITY can be bounded in terms of the number of mistakes made by the best prediction algorithm in the pool

Much early work on computational learning theory dealt with the question of whether the learner could identify the target concept in the limit, given an indefinitely long sequence of training examples The identification in the limit model was introduced by Gold (1967) A good overview of results in this area is (Angluin 1992) Vapnik (1982) examines in detail the problem of uniform convergence, and the closely related PAC-learning model was introduced by Valiant (1984) The discussion in this chapter of -exhausting the version space is based on Haussler's (1988) exposition A useful collection of results under the PAC model can be found in Blumer et al (1989) Kearns and Vazirani (1994) provide an excellent exposition of many results from computational learning theory Earlier texts in this area include Anthony and Biggs (1992) and Natarajan (1991)

Next you need to wire up the application. Remember in 3 when you connected the button to the controller, and the controller to the source and destination Text Fields That s what needs to happen now. But where is the controller There is no obvious dedicated controller in the Document window. The answer in this case is File s Owner. File s Owner is a special object that represents the object (in this case MainViewController) that owns or loads the user interface. You can confirm this by selecting File s Owner and looking at the Identity panel in the Inspector window. You will see that it is an instance of a MainViewController. So this is the controller we want to make our connections to. This may seem odd, since in a conventional Mac OS X application File s Owner is an instance of NSApplication, but in this simple type of iPhone application File s Owner is an instance of MainViewController (you can confirm this by selecting File s Owner and looking at the Identity panel in the Inspector window you will see that it is an instance of a MainViewController). That s where we want to make our connections. Control-drag from the UIButton to File s Owner. When you drop the line on File s Owner, choose the sayHello: action (see Figure 4 7).

Current research on computational learning theory covers a broad range of learning models and learning algorithms Much of this research can be found in the proceedings of the annual conference on Computational Learning Theory (COLT) Several special issues of the journal Machine Learning have also been devoted to this topic

literary interests continued work in writing and poetry, focusing on the development of craft, image, and voice; study of psychological and cultural complexities of poems and poets; interest in works of pound, williams, lowell, bishop, plath, hayden, ginsberg, and rich

Often we are interested in comparing the performance of two learning algorithms L A and L B , rather than two specific hypotheses What is an appropriate test for comparing learning algorithms, and how can we determine whether an observed difference between the algorithms is statistically significant Although there is active debate within the machine-learning research community regarding the best method for comparison, we present here one reasonable approach A discussion of alternative methods is given by Dietterich (1996) As usual, we begin by specifying the parameter we wish to estimate Suppose we wish to determine which of LA and LB is the better learning method on average for learning some particular target function f A reasonable way to define "on average" is to consider the relative performance of these two algorithms averaged over all the training sets of size n that might be drawn from the underlying instance distribution VIn other words, we wish to estimate the expected value