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As illustrated in the above section, Bayesian analysis can sometimes be used to show that a particular learning algorithm outputs MAP hypotheses even though it may not explicitly use Bayes rule or calculate probabilities in any form In this section we consider the problem of learning a continuous-valued target function-a problem faced by many learning approaches such as neural network learning, linear regression, and polynomial curve fitting A straightforward Bayesian analysis will show that under certain assumptions any learning algorithm that minimizes the squared error between the output hypothesis predictions and the training data will output a maximum likelihood hypothesis The significance of this result is that it provides a Bayesian justification (under certain assumptions) for many neural network and other curve fitting methods that attempt to minimize the sum of squared errors over the training data Consider the following problem setting Learner L considers an instance space X and a hypothesis space H consisting of some class of real-valued functions defined over X (ie, each h in H is a function of the form h : X -+ 8, where 8 represents the set of real numbers) The problem faced by L is to learn an unknown target function f : X -+ 8 drawn from H A set of m training examples is provided, where the target value of each example is corrupted by random noise drawn according to a Normal probability distribution More precisely, each training example is a pair of the form (xi,d i ) where di = f (xi) ei Here f (xi)is the noise-free value of the target function and ei is a random variable representing the noise It is assumed that the values of the ei are drawn independently and that they are distributed according to a Normal distribution with zero mean The task of the learner is to output a maximum likelihood hypothesis, or, equivalently, a MAP hypothesis assuming all hypotheses are equally probable a priori A simple example of such a problem is learning a linear function, though our analysis applies to learning arbitrary real-valued functions Figure 62 illustrates.

A table knows a row is editable by the tableView:canEditRowAtIndexPath: method If you wish all rows to be editable, simply have the method return YES; otherwise, implement code to determine if a particular row is editable If you omit this method, no rows are editable The tableView:editingStyleForRowAtIndexPath: method informs the table what style editing accessory the row should have If this method returns a UITableViewCellEditingStyleNone,

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the Zoran software DVD player to get to the decrypted, decompressed video Zoran plugged the leak, but pirated copies of the earlier Zoran player were available on the Internet Still, the process was so complicated that only the most dedicated hackers were interested in spending half a day of their time copying a two-hour movie The continuing difficulty of getting equipment to encode multichannel MPEG audio finally became such a problem that on December 5, 1997, the DVD Forum Steering Committee voted to amend the DVD specification to add Dolby Digital as one of the mandatory audio format options on 625/50 (PAL/SECAM) discs Previously, PAL/SECAM discs had to have either PCM or MPEG audio tracks The change allowed disc producers to exclusively use Dolby Digital soundtracks Philips, the primary supporter of MPEG audio, and its partner Sony voted against the change, but they were overruled by the other eight companies There was scattered cheering among European DVD enthusiasts who preferred the much wider selection of Dolby Digitalequipped audio gear There was grumbling from Philips, which said, The decision made is based on incorrect information Philips will continue to support MPEG-2 multichannel audio for PAL/SECAM countries In December, Microsoft released DirectShow 52 (also called DirectShow 20), an extension to the Windows operating system that supported DVD playback Up to this point, vendors of DVD hardware and software for Windows PCs had used the old MCI system, each implementing things slightly differently DirectShow promised to provide a robust and standard interface for developers of DVD software, along with support for the full DVDVideo feature set Unfortunately, as it turned out, most DVD software decoder vendors were more interested in signing OEM deals to get their software bundled into new PCs than in supporting Microsoft s standard The shift from proprietary decoders to DirectShow-compatible decoders, which should have happened in less than a year, barely reached the threequarters point three years later The resulting lack of a single DVD development target for Windows contributed to the retarded growth of multimedia DVD software At the end of December, 340,000 players had been sold in the US This was below expectations of 12 million Dozens of reports appeared, lamenting or disparaging DVD s performance Few bothered to mention that most of the expectations had been unreasonably optimistic Given that DVD had been launched only nine months previously, with the first five months limited to seven test cities, player sales were actually quite impressive Rockley Miller s predicted cycles of euphoria leading to disillusionment were holding true.

FIGURE 62 Learning a real-valued function The target function f corresponds to the solid line The training examples (xi, ) are assumed di to have Normally distributed noise ei with zero mean added to the true target value f (xi) The dashed line corresponds to the linear function that minimizes the sum of squared errors Therefore, it is the maximum likelihood hypothesis ~ M L given these five , training examples

Figure 10-27

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