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decisions regarding preconditions of different rules may be more effective An additional consideration is the task-specific question of whether it is desirable that different rules test the same attributes In the simultaneous covering decision tree learning algorithms, they will In sequential covering algorithms, they need not A second dimension along which approaches vary is the direction of the search in LEARN-ONE-RULE algorithm described above, the search is from In the general to specijic hypotheses Other algorithms we have discussed (eg, FIND-S from 2) search from specijic to general One advantage of general to specific search in this case is that there is a single maximally general hypothesis from which to begin the search, whereas there are very many specific hypotheses in most hypothesis spaces (ie, one for each possible instance) Given many maximally specific hypotheses, it is unclear which to select as the starting point of the search One program that conducts a specific-to-general search, called GOLEM (Muggleton and Feng 1990), addresses this issue by choosing several positive examples at random to initialize and to guide the search The best hypothesis obtained through multiple random choices is then selected A third dimension is whether the LEARN-ONE-RULE is a generate then search test search through the syntactically legal hypotheses, as it is in our suggested implementation, or whether it is example-driven so that individual training examples constrain the generation of hypotheses Prototypical example-driven search algorithms include the FIND-Sand CANDIDATE-ELIMINATION algorithms of Chapalgorithm discussed later in this chapter ter 2, the AQ algorithm, and the CIGOL In each of these algorithms, the generation or revision of hypotheses is driven by the analysis of an individual training example, and the result is a revised hypothesis designed to correct performance for this single example This contrasts to the generate and test search of LEARN-ONE-RULE in Table 102, in which successor hypotheses are generated based only on the syntax of the hypothesis representation The training data is considered only after these candidate hypotheses are generated and is used to choose among the candidates based on their performance over the entire collection of training examples One important advantage of the generate and test approach is that each choice in the search is based on the hypothesis performance over many examples, so that the impact of noisy data is minimized In contrast, example-driven algorithms that refine the hypothesis based on individual examples are more easily misled by a single noisy training example and are therefore less robust to errors in the training data A fourth dimension is whether and how rules are post-pruned As in decision tree learning, it is possible for LEARN-ONE-RULE to formulate rules that perform very well on the training data, but less well on subsequent data As in decision tree learning, one way to address this issue is to post-prune each rule after it is learned from the training data In particular, preconditions can be removed from the rule whenever this leads to improved performance over a set of pruning examples distinct from the training examples A more detailed discussion of rule post-pruning is provided in Section 3712. code 39 font c# C# Imaging - C# Code 39 Barcoding Tutorial - RasterEdge.com
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C# .NET Code 39 generator to specify Code 39 images in Winforms and Web Forms, generate and save Code 39 in png, jpeg, gif, tiff, bmp image formats. No effect on normal use of system Low additional resource requirement Tamper-resistant Voluntary Low cost Requirements of consumers are: Listing 2-5 No effect on normal use of system No loss of quality Fair-use copying No additional cost No limitations on playback equipment or environment A final dimension is the particular definition of rule PERFORMANCE used to Various evaluation functions have been used guide the search in LEARN-ONE-RULE Some common evaluation functions include: int sayHello() { printf("hello programmer%d\n", MYNUMBER); return 0; } c# code 39 generator C# Code 39 Generator Library for .NET - BarcodeLib.com
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Developer guide for generating Code 39 barcode images in .NET applications using Visual C# . Code 39 C# barcoding examples for ASP.NET website ... Relative frequency is used to evaluate rules in the AQ program m-estimate of accuracy This accuracy estimate is biased toward the default accuracy expected of the rule It is often preferred when data is scarce and the rule must be evaluated based on few examples As above, let n and nc denote the number of examples matched and correctly predicted by the rule Let p be the prior probability that a randomly drawn example from the entire data set will have the classification assigned by the rule (eg, if 12 out of 100 examples have the value predicted by the rule, then p = 12) Finally, let m be the weight, or equivalent number of examples for weighting this prior p The m-estimate of rule accuracy is . You define constants in header files using the #define preprocessor directive You don t include a constant s type when defining a constant c# create code 39 barcode Code 39 C# SDK Library - Code 39 barcode image generator using ...
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