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DVD Consortium would be signed at a June 3 meeting Apparently, however, the agreement had only been that all three parties would refrain from introducing copy protection legislation before the June 3 meeting, and Toshiba officials later denied the reports of any sort of settlement Despite all this, executives at Thomson said that they were confident the issues would be resolved and that they were ready for launch as early as summer As DVD progress was being attacked by the popular press, standardization efforts were having their own problems Sony threw a monkey wrench into the process by proposing a completely new DVD-Audio format, Direct Stream Digital (DSD) Sony claimed that its single-bit format was not tied to specific sampling frequencies and sizes, thus eliminating downsampling and oversampling and resulting in less noise The ARA did not agree and continued to push for PCM Then, in the May 1996 issue of CD-ROM Professional, guest columnist Hugh Bennet publicized a serious flaw that had apparently gone unrecognized or was being ignored by DVD engineers The dye used in recordable CD media (CD-R) did not reflect the smaller-wavelength laser light used in DVD, thus rendering the discs invisible He pointed out that more than 2 million CD recorders were expected to be in use by the end of 1996 and that the recorders were expected to have written between 75 and 100 million CD-R discs by then A new type II CD-R that would work with CD and DVD had been proposed but would take years to supersede the current format, leaving millions of CD-R users out in the cold when it came time to switch to DVD readers

One practical difficulty in applying Bayesian methods is that they typically require initial knowledge of many probabilities When these probabilities are not known in advance they are often estimated based on background knowledge, previously available data, and assumptions about the form of the underlying distributions A second practical difficulty is the significant computational cost required to determine the Bayes optimal hypothesis in the general case (linear in the number of candidate hypotheses) In certain specialized situations, this computational cost can be significantly reduced

16 Modify the tableView:cellForRowAtIndexPath: method to set the cell s accessory,

uum The lacquer is hardened by UV light This forms an extremely strong bond that is unaffected by temperature A transparent adhesive such as 2P is required for dual-layer discs, since the laser must be able to read through it The silk-screening method is more appropriate for writable discs because the cationic adhesive curing process generates less heat, which is less likely to affect the dye or phase-change layers Similar bonding techniques are used with both magneto-optical (MO) discs and laserdiscs Laserdiscs sometimes suffer from deterioration of the aluminum layer, which is colloquially referred to as laser rot even though it has nothing to do with the laser beam or with rotting Laserdiscs are molded from acrylic (PMMA), which absorbs about 10 times more moisture than the polycarbonate used in DVDs and can lead to oxidation of the aluminum The flexibility of laserdiscs allows movement along the bond, which also can damage the aluminum layer Laser rot problems were more prevalent in the past when material purity was lower and the interaction between the materials that make up a disc was not as well understood as it is today Since DVDs are molded from improved materials and are more rigid than laserdiscs, they are not as susceptible to laser rot

17 Click Build And Go The table cells have the customized appearance (Figure 10-22)

The remainder of this chapter is organized as follows Section 62 introduces Bayes theorem and defines maximum likelihood and maximum a posteriori probability hypotheses The four subsequent sections then apply this probabilistic framework to analyze several issues and learning algorithms discussed in earlier chapters For example, we show that several previously described algorithms output maximum likelihood hypotheses, under certain assumptions The remaining sections then introduce a number of learning algorithms that explicitly manipulate probabilities These include the Bayes optimal classifier, Gibbs algorithm, and naive Bayes classifier Finally, we discuss Bayesian belief networks, a relatively recent approach to learning based on probabilistic reasoning, and the EM algorithm, a widely used algorithm for learning in the presence of unobserved variables