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marker can only go at the beginning of a program3 For a multi-PGC title, the user may take different paths from PGC to PGC, so chapter 2 via one path may be made up of different PGCs than 2 via another path (see Figure 619) Three types of titles exist: a monolithic title meant to be played straight through (one_sequential_PGC title), a title with multiple PGCs for varying program flow (multi_PGC title), and a title with multiple PGCs that are automatically selected according to the parental restriction setting of the player (parental_block title) (see Figures 620 and 621) One_sequential_ PGC titles are the only kind that have time maps for timecode display and searching Figure 622 shows the relationship between presentation data and physical data

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Figure 10-19

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Notice two key differences between this estimator and the quantity in Equation (514) First, we are using errorTo(h)to approximate errorv(h) Second, we are only measuring the difference in errors for one training set So rather than taking the expected value of this difference over all samples S that might be drawn ) from the distribution 2 One way to improve on the estimator given by Equation (515) is to repeatedly partition the data Do into disjoint training and test sets and to take the mean of the test set errors for these different experiments This leads to the procedure shown in Table 55 for estimating the difference between errors of two learning methods, based on a fixed sample Do of available data This procedure first partitions the data into k disjoint subsets of equal size, where this size is at least 30 It then trains and tests the learning algorithms k times, using each of the k subsets in turn as the test set, and using all remaining data as the training set In this way, the learning algorithms are tested on k independent test sets, 'and the mean difference in errors 8 is returned as an estimate of the difference between the two learning algorithms The quantity 8 returned by the procedure of Table 55 can be taken as an estimate of the desired quantity from Equation 514 More appropriately, we can view 8 as an estimate of the quantity

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Navigation data is a collection of information that determines how the physical data is accessed In a sense, navigation data is built on top of presentation data Navigation data controls access and interactive playback It is grouped into four categories: control, search, user interface, and navigation commands (see Figure 623) There are five levels at which navigation data exists: video manager information (VMGI), stored in the VMG, which controls the video title sets and the title menu; video title-set information (VTSI), stored in each VTS, which controls the titles and menus in a VTS; program chain information (PGCI), stored in the PGC, which controls access to components of a PGC such as audio and subpicture streams; presentation control information (PCI), stored in packets spread throughout the data stream (one per VOBU), which controls menu display and program presentation in real time; and data search information (DSI), also stored in packets spread throughout the data stream (one per VOBU), which controls forward/reverse scanning and seamless branching (see Table 611) Previous, next, and return (go up) PGC information is stored in the PGCI The return link corresponds to the Return button on the remote control The Previous ( ) and Next ( ) buttons on the remote control jump between programs until there is no previous or next program, in

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ID01 drawn uniformly from Do where S represents a random sample of size The only difference between this expression and our original expression in Equation (514) is that this new expression takes the expected value over subsets of the available data Do, rather than over subsets drawn from the full instance dis) tribution 2

In general, because chapters must start on a program boundary, a chapter is equivalent to a program Specifically, every chapter is a program, but not every program is a chapter The Next and Previous keys on remote controls jump between programs, not chapters

10:

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1 Partition the available data Do into k disjoint subsets T I , T2, ,Tk of equal size, where this size is at least 30 2 For i from 1 to k, do use Ti for the test set, and the remaining data for training set Si 0 Si c {Do - Ti}

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