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Znstances are represented by attribute-valuepairs Instances are described by a fixed set of attributes (eg, Temperature) and their values (eg, Hot) The easiest situation for decision tree learning is when each attribute takes on a small number of disjoint possible values (eg, Hot, Mild, Cold) However, extensions to the basic algorithm (discussed in Section 372) allow handling real-valued attributes as well (eg, representing Temperature numerically) The targetfunction has discrete output values The decision tree in Figure 31 assigns a boolean classification (eg, yes or no) to each example Decision tree methods easily extend to learning functions with more than two possible output values A more substantial extension allows learning target functions with real-valued outputs, though the application of decision trees in this setting is less common Disjunctive descriptions may be required As noted above, decision trees naturally represent disjunctive expressions The training data may contain errors Decision tree learning methods are robust to errors, both errors in classifications of the training examples and errors in the attribute values that describe these examples The training data may contain missing attribute values Decision tree methods can be used even when some training examples have unknown values (eg, if the Humidity of the day is known for only some of the training examples) This issue is discussed in Section 374

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A DVD-Video disc plays in any DVD system: DVD players, DVD computers, DVD game consoles, and so on It has more limitations, making project design and production easier in some ways In general, DVD-Video discs require no tech support DVD-ROM has more flexibility, and it has better support for text display, data manipulation, searching, and interactivity The best approach is to combine both, using the best qualities of each Of course, this option requires more work See 11 for more on computer use of DVD and enhancing DVD-Video and DVD-Audio discs with Internet technology

(Figure 6-2)

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DVD-Video and DVD-Audio production require specialized equipment and software Melding the computer world and the audio/video world, in ways that are unfamiliar to many, is a complex process Computer graphics artists, accustomed to square pixels and an RGB palette, must learn about non-square video pixels, video-safe colors, overscan, and interlace artifacts Video engineers, accustomed to headroom and waveforms, have to deal with YCbCr colorspace, sampling, compression, and bit rates Audio engineers also must deal with issues such as multichannel mixing, apportioning the LFE channel, downmixing, sampling frequencies and word sizes, and synchronizing multiple audio tracks to video DVD production work is done in a variety of ways and places Each business model is optimized for the market it serves, with attendant advantages and disadvantages Smaller businesses may provide services for only part of the DVD production process, such as menu design, subtitles, or audio formatting, while large service houses can provide a one-stop shopping experience for an entire project Several types of DVD production environments are available

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The J4L Barcodes are integrated in Eclipse Birt 4.3 or later. The components support 1D barcodes, PDF417, Datamatrix , QRCode, Azteccode and Maxicode.

You could have created the view controller first and then checked the With XIB for user interface, check box and Xcode would have generated the xib for you 7 Name the file FirstViewControllerm and ensure the Also Create FirstViewControllerh

Many practical problems have been found to fit these characteristics Decision tree learning has therefore been applied to problems such as learning to classify medical patients by their disease, equipment malfunctions by their cause, and loan applicants by their likelihood of defaulting on payments Such problems, in which the task is to classify examples into one of a discrete set of possible categories, are often referred to as classijication problems The remainder of this chapter is organized as follows Section 34 presents the basic ID3 algorithm for learning decision trees and illustrates its operation in detail Section 35 examines the hypothesis space search performed by this learning algorithm, contrasting it with algorithms from 2 Section 36 characterizes the inductive bias of this decision tree learning algorithm and explores more generally an inductive bias called Occam's razor, which corresponds to a preference for the most simple hypothesis Section 37 discusses the issue of overfitting the training data, as well as strategies such as rule post-pruning to deal with this problem This section also discusses a number of more advanced topics such as extending the algorithm to accommodate real-valued attributes, training data with unobserved attributes, and attributes with differing costs

Movie/music studios or subsidiaries develop their own products from content they create Film/video/audio post-production houses produce DVD as a sideline to complement core business Replication facilities operate authoring services to generate more disc manufacturing business and can provide one-stop shopping DVD shops focus primarily on encoding and authoring Specialty DVD shops focus on market niches such as government, museums, or weddings Corporate A/V departments develop discs for internal training or external marketing and may provide production services to other companies Individual or small-group projects involve one person or a few people with a vision, such as independent filmmakers

(continued)