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Video produced on a computer, or camera-source video that is composited with computer graphics, has its own set of issues The most important thing to keep in mind is avoiding too much detail, especially vertical detail, which wreaks havoc on interlaced TVs Avoid thin horizontal lines; use the antialiasing and motion blur features of your animation or editing software whenever possible Use a low-pass or blur filter, but don t overdo it because too much blurring reduces resolution on progressive-scan players and computers For 525-line (NTSC) video, render animations at 720 540 and scale down to 720 480 before encoding For 625-line (PAL) video, render animations at 768 576 and scale down to 720 576 before encoding, or if your software supports it, render with D1 or DV pixel geometry

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#import "SimpleViewAppDelegateh" #import "SimpleViewViewControllerh" @implementation SimpleViewAppDelegate @synthesize window; @synthesize viewController; - (void)applicationDidFinishLaunching:(UIApplication *)application { [window addSubview:viewControllerview]; [window makeKeyAndVisible]; } - (void)dealloc { [viewController release]; [window release]; [super dealloc]; } @end

If at all possible, do not use the print to video or similar TV video rendering feature of your software, which will almost always result in interlaced output Instead, render at 24 fps progressive or 30 fps progressive, then encode for 2997 fps display This requires an encoder that can handle progressive video input files and perform synthetic telecine to add flags to the MPEG-2 stream for 2-3 pulldown in the player (see the following graphics preparation section for more details)

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The View-based Application template hides many development details If new to iPhone programming, chances are the View-based Application template does not help clarify a UIView, UIViewController, and their relationship To help make their relationship clearer, you should understand what the View-based Application template accomplishes automatically Unlike a View-based Application template, a Window-based Application template requires understanding UIViews and UIViewControllers When using the Window-based Application template, you must manually create a view and view controller and wire them together In this project, you create a single view application starting with a Window-based Application template Creating a Window-based Application should solidify your understanding of the steps behind its creation

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Given the original error-free data, ID3 produces the decision tree shown in Figure 31 However, the addition of this incorrect example will now cause ID3 to construct a more complex tree In particular, the new example will be sorted into the second leaf node from the left in the learned tree of Figure 31, along with the previous positive examples D9 and D l 1 Because the new example is labeled as a negative example, ID3 will search for further refinements to the tree below this node Of course as long as the new erroneous example differs in some arbitrary way from the other examples affiliated with this node, ID3 will succeed in finding a new decision attribute to separate out this new example from the two previous positive examples at this tree node The result is that ID3 will output a decision tree (h) that is more complex than the original tree from Figure 31 (h') Of course h will fit the collection of training examples perfectly, whereas the simpler h' will not However, given that the new decision node is simply a consequence of fitting the noisy training example, we expect h to outperform h' over subsequent data drawn from the same instance distribution The above example illustrates how random noise in the training examples can lead to overfitting In fact, overfitting is possible even when the training data are noise-free, especially when small numbers of examples are associated with leaf nodes In this case, it is quite possible for coincidental regularities to occur, in which some attribute happens to partition the examples very well, despite being unrelated to the actual target function Whenever such coincidental regularities exist, there is a risk of overfitting Overfitting is a significant practical difficulty for decision tree learning and many other learning methods For example, in one experimental study of ID3 involving five different learning tasks with noisy, nondeterministic data (Mingers 1989b), overfitting was found to decrease the accuracy of learned decision trees by 10-25% on most problems There are several approaches to avoiding overfitting in decision tree learning These can be grouped into two classes: approaches that stop growing the tree earlier, before it reaches the point where it perfectly classifies the training data, approaches that allow the tree to overfit the data, and then post-prune the tree.

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1 Create a new Window-based Application and name it SimpleWindow 2 Highlight Resources and create an empty xib Name the xib FirstViewControllerxib 3 Highlight Classes and add a UIViewController named FirstViewController Xcode should

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