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As in earlier chapters, let X refer to the set of all possible instances over which target functions may be defined For example, X might represent the set of all people, each described by the attributes age (eg, young or old) and height (short or tall) Let C refer to some set of target concepts that our learner might be called upon to learn Each target concept c in C corresponds to some subset of X, or equivalently to some boolean-valued function c : X + {0, 1) For example, one target concept c in C might be the concept "people who are skiers" If x is a positive example of c, then we will write c(x) = 1; if x is a negative example, c(x) = 0 We assume instances are generated at random from X according to some probability distribution DFor example, 2)might be the distribution of instances generated by observing people who walk out of the largest sports store in Switzerland In general, D may be any distribution, and it will not generally be known to the learner All that we require of D is that it be stationary; that is, that the distribution not change over time Training examples are generated by drawing an instance x at random according to D,then presenting x along with its target value, c(x), to the learner The learner L considers some set H of possible hypotheses when attempting to learn the target concept For example, H might be the set of all hypotheses

The disc may be a type that is not physically readable in the computer drive DVD players use the UDF file system to find and read the files containing audio and video The computer may not have used UDF DVD players also expect the files to be physically contiguous The computer may not have ordered the video files in contigous order from the beginning of the disc The data may be recorded as a set of MPEG-2 files or MP3 files, which the player is not designed to recognize and process The disc may be a type that is not physically readable in the computer drive, such as DVD-RAM or DVD+RW The second computer may not recognize the file system used by the first For example, newer versions of Windows natively use FAT32, which many other operating systems cannot read The second computer may not have the software needed to play the disc Bugs may be present in the drivers or formatting software of the first or second computer

protocol (Listings 12-10 and 12-11)

File system compatibility is generally not a problem The file system determines how the data is organized and accessed UDF and ISO 9660 are the

describable by conjunctions of the attributes age and height After observing a sequence of training examples of the target concept c, L must output some hypothesis h from H, which is its estimate of c To be fair, we evaluate the success of L by the performance of h over new instances drawn randomly from X according to D, the same probability distribution used to generate the training data Within this setting, we are interested in characterizing the performance of various learners L using various hypothesis spaces H, when learning individual target concepts drawn from various classes C Because we demand that L be general enough to learn any target concept from C regardless of the distribution of training examples, we will often be interested in worst-case analyses over all possible target concepts from C and all possible instance distributions D

3 Open MyWebViewControllerxib in Interface Builder 4 Add a Web View from the Library to the canvas Also add a text field and a button

two common file systems on DVD; in fact, most discs contain both Because DVDs are simply storage media, other file systems such as Microsoft FAT, NTFS, Macintosh HFS, UNIX, and so on can be used to write data files to the disc Compatibility problems generally occur only with these specialized file systems, such as when a disc formatted with Windows FAT32 is placed in the drive of a Mac

(Figure 12-29)

Because we are interested in how closely the learner's output hypothesis h approximates the actual target concept c, let us begin by defining the true error of a hypothesis h with respect to target concept c and instance distribution D Informally, the true error of h is just the error rate we expect when applying h to future instances drawn according to the probability distribution 2 In fact, we 7 already defined the true error of h in 5 For convenience, we restate the definition here using c to represent the boolean target function