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(c) Assume the learner has encountered only the positive example from part (b), and that it is now allowed to query the trainer by generating any instance and asking the trainer to classify it Give a specific sequence of queries that assures the learner will converge to the single correct hypothesis, whatever it may be (assuming that the target concept is describable within the given hypothesis language) Give the shortest sequence of queries you can find How does the length of this sequence relate to your answer to question (b) (d) Note that this hypothesis language cannot express all concepts that can be defined over the instances (ie, we can define sets of positive and negative examples for which there is no corresponding describable hypothesis) If we were to enrich the language so that it could express all concepts that can be defined over the instance language, then how would your answer to (c) change 26 Complete the proof of the version space representation theorem (Theorem 21) Consider a concept learning problem in which each instance is a real number, and in which each hypothesis is an interval over the reals More precisely, each hypothesis in the hypothesis space H is of the form a < x < b, where a and b are any real constants, and x refers to the instance For example, the hypothesis 45 < x < 61 classifies instances between 45 and 61 as positive, and others as negative Explain informally why there cannot be a maximally specific consistent hypothesis for any set of positive training examples Suggest a slight modification to the hypothesis representation so that there will be 'C

At the end of this process I had a new Fixed copy folder that held the files containing the correct versions I opened the project and checked that all was well FileMerge is a sophisticated and capable utility, if a little quirky in usability terms If you need an effective method to manage the business of comparing and merging the contents of folders and text files, it s there waiting for you..

28 In this chapter, we commented that given an unbiased hypothesis space (the power set of the instances), the learner would find that each unobserved instance would match exactly half the current members of the version space, regardless of which training examples had been observed Prove this In particular, prove that for any instance space X, any set of training examples D, and any instance x E X not present will in D, that if H is the power set of X, then exactly half the hypotheses in V S H , D classify x as positive and half will classify it as negative 29 Consider a learning problem where each instance is described by a conjunction of n boolean attributes a1 a, Thus, a typical instance would be

A (a, = T)

Once you have finished, turn off VoiceOver and then run Accessibility Verifier against the DailyJournal application again. This time you should see a more healthy report (see Figure 14 10). The figure shows the section of the report dealing with the controls where you have added Help and Description tags. Notice that the report has picked up these tags rather than the content of the control (see, for example, the AXDateTimeArea and compare this with Figure 14 5).

Now consider a hypothesis space H in which each hypothesis is a disjunction of constraints over these attributes For example, a typical hypothesis would be

MDiv, GRADUATE THEOLOGICAL UNION, Berkeley, CA Major: Cultural and Historical Study of Religion Thesis: History of Religion in America: 1980 1990 Coursework: Religion, Fundamentalism, and Nationalism Modern Western Religious Thought Religion and Anthropology History of Religion in America Since 1865 Ethnicity, Race, and Religion in America Public Religion in US History Sufism Topics in Comparative Religions Buddhism Understanding World Religions in Multicultural Contexts BA, UNIVERSITY OF MELBOURNE, Melbourne, Australia Major: Asian Languages and Literatures Specialty: Chinese Language and Literatures

Propose an algorithm that accepts a sequence of training examples and outputs a consistent hypothesis if one exists Your algorithm should run in time that is polynomial in n and in the number of training examples 210 Implement the FIND-Salgorithm First verify that it successfully produces the trace in Section 24 for the Enjoysport example Now use this program to study the number of random training examples required to exactly learn the target concept Implement a training example generator that generates random instances, then classifies them according to the target concept:

So you can enhance the basic behavior of your controls for VoiceOver simply by adding these two tags to the Identity information for the control. It's a good accessibility practice to add Description and Help tags to all of the controls that your user is going to interact with, but particularly where controls could be confused. A similar situation exists for image buttons. If there is not even a title on the button, VoiceOver has nothing much to go on in helping the user. Adding those Description and Help tags will make all the difference between an accessible application and a frustrating one. Having said all that, your user may find it annoying to have VoiceOver droning on interminably on every control, so keep the messages crisp and to the point.

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