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Previous chapters have considered a variety of inductive learning methods: that is, methods that generalize from observed training examples by identifying features that empirically distinguish positive from negative training examples Decision tree learning, neural network learning, inductive logic programming, and genetic

2 (defining one of four types) Background, foreground, emphasis-1, emphasis-2 4 of 16 (from 4-bit palette,b one per type) 4 of 16 (from 4-bit palette,b one per type)

(continued)

6

.

Listing 15-3

transparency is set directly, from invisible (0), through 14 levels of transparency (1 14), to opaque (15) The display area (starting coordinates, width, and height) can be specified up to almost a full-screen rectangle (0,0,720,478 or 0,0,720,573) The display area and content (bitmap) can be changed for each frame or field The color and contrast of the four pixel types can be changed for each frame or field; the palettes can be changed for each PGC The maximum data rate for a single subpicture stream is 336 Mbps, with a maximum size per frame of 53,220 bytes Each run-length-encoded line can be up to 1440 bits (720 2) Associated display control sequences (DCSQs) can be up to 32,000 bytes (see Figure 632)

algorithms are all examples of inductive methods that operate in this fashion The key practical limit on these inductive learners is that they perform poorly when insufficient data is available In fact, as discussed in 7, theoretical analysis shows that there are fundamental bounds on the accuracy that can be achieved when learning inductively from a given number of training examples Can we develop learning methods that are not subject to these fundamental bounds on learning accuracy imposed by the amount of training data available Yes, if we are willing to reconsider the formulation of the learning problem itself One way is to develop learning algorithms that accept explicit prior knowledge as an input, in addition to the input training data Explanation-based learning is one such approach It uses prior knowledge to analyze, or explain, each training example in order to infer which example features are relevant to the target function and which are irrelevant These explanations enable it to generalize more accurately than inductive systems that rely on the data alone As we saw in the previous chapalso ter, inductive logic programming systems such as CIGOL use prior background knowledge to guide learning However, they use their background knowledge to infer features that augment the input descriptions of instances, thereby increasing the complexity of the hypothesis space to be searched In contrast, explanationbased learning uses prior knowledge to reduce the complexity of the hypothesis space to be searched, thereby reducing sample complexity and improving generalization accuracy of the learner To capture the intuition underlying explanation-based learning, consider the task of learning to play chess In particular, suppose we would like our chess program to learn to recognize important classes of game positions, such as the target concept "chessboard positions in which black will lose its queen within two moves" Figure 111 shows a positive training example of this target concept Inductive learning methods could, of course, be employed to learn this target concept However, because the chessboard is fairly complex (there are 32 pieces that may be on any of 64 squares), and because the particular patterns that capture this concept are fairly subtle (involving the relative positions of various pieces on the board), we would have to provide thousands of training examples similar to the one in Figure 111 to expect an inductively learned hypothesis to generalize correctly to new situations.

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A typical set of subtitles are presented at about 14 per minute, for around 1500 per movie The subpicture display commands (DCCs) are used to change the location, scroll position, transparency, and so on A sequence of commands can be used to create effects such as color changes, moving highlights, fades, crawls, and so on

Using the writeToFile: method to save a property list object as a simple property list is usually sufficient, but another way you can persist a property list object to a property list is by using the NSPropertyListSerialization class