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A section near the hub of the disc, called the burst cutting area (BCA), optionally can be used for individualizing discs during the replication process A strong laser is used to cut a series of stripes, somewhat like a bar code, to store up to 188 bytes of information such as ID codes or serial numbers The BCA sits between 446 and 47 millimeters from the center of the disc (Figure 54) The BCA can be read by the same laser pickup head that reads the disc BCA information can be used for inventory purposes or by storage systems such as disc jukeboxes to quickly identify discs The BCA is also used by copy protection systems to uniquely encode the data on recordable discs so that they can be decoded only with a key stored in the BCA Manufacturers of recordable media write a unique media ID in the BCA of each disc During the development of this system, the DVD Forum realized that data in the BCA needed to be organized because the existing free-for-all would cause problems They defined a BCA pack format, where the first 2 bytes of each pack hold an application ID to identify the chunk of data stored in the pack The third byte is a version number, the fourth byte indicates the length of the data in the pack, and remaining bytes of the pack are the data This allows multiple packs to be placed in the

Listing 10-19

5

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#import <UIKit/UIKith> @interface MyCellView : UITableViewCell { IBOutlet UILabel * theTextLabel; } @property (nonatomic, retain) IBOutlet UILabel * theTextLabel; @end

BCA Various applications can read each pack until they find one that has the desired application ID A variation called narrow burst cutting area (NBCA) is used by DVD-RW and possibly other writable DVD formats NBCA barcodes are cut in the 227- to 235-millimeter-radius section

In machine learning we are often interested in determining the best hypothesis from some space H, given the observed training data D One way to specify what we mean by the best hypothesis is to say that we demand the most probable hypothesis, given the data D plus any initial knowledge about the prior probabilities of the various hypotheses in H Bayes theorem provides a direct method for calculating such probabilities More precisely, Bayes theorem provides a way to calculate the probability of a hypothesis based on its prior probability, the probabilities of observing various data given the hypothesis, and the observed data itself To define Bayes theorem precisely, let us first introduce a little notation We shall write P ( h ) to denote the initial probability that hypothesis h holds, before we have observed the training data P(h) is often called the priorprobability of h and may reflect any background knowledge we have about the chance that h is a correct hypothesis If we have no such prior knowledge, then we might simply assign the same prior probability to each candidate hypothesis Similarly, we will write P ( D ) to denote the prior probability that training data D will be observed (ie, the probability of D given no knowledge about which hypothesis holds) Next, we will write P(D1h) to denote the probability of observing data D given some world in which hypothesis h holds More generally, we write P(xly) to denote the probability of x given y In machine learning problems we are interested in the probability P ( h1 D ) that h holds given the observed training data D P (h1 D ) is called the posteriorprobability of h, because it reflects our confidence that h holds after we have seen the training data D Notice the posterior probability P(h1D) reflects the influence of the training data D, in contrast to the prior probability P(h), which is independent of D Bayes theorem is the cornerstone of Bayesian learning methods because it provides a way to calculate the posterior probability P(hlD), from the prior probability P(h), together with P ( D ) and P ( D ( h ).

Listing 10-20

A critical component of DVD units is the optical pickup, which houses the laser (or lasers), optical elements, and associated electronic circuitry The

#import "MyCellViewh" @implementation MyCellView @synthesize theTextLabel;

Bayes theorem:

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(continued)