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recognize DNA segments called promoter regions, which influence gene activity In this experiment KBANN was given an initial domain theory obtained from a molecular geneticist, and a set of 53 positive and 53 negative training examples of promoter regions Performance was evaluated using a leave-one-out strategy in which the system was run 106 different times On each iteration KBANN was trained using 105 of the 106 examples and tested on the remaining example The results of these 106 experiments were accumulated to provide an estimate of the true error rate KBANN obtained an error rate of 41106, compared to an error rate A variant of the KBANN approach was of 81106 using standard BACKPROPAGATION applied by Fu (1993), who reports an error rate of 21106 on the same data Thus, the impact of prior knowledge in these experiments was to reduce significantly the error rate The training data for this experiment is available at World Wide Web site http:llwwwicsuciedu/~mlearn/MLRepositoryhtml Both Fu (1993) and Towel1 et al (1990) report that Horn clauses extracted from the final trained network provided a refined domain theory that better fit the observed data Although it is sometimes possible to map from the learned network weights back to a refined set of Horn clauses, in the general case this is problematic because some weight settings have no direct Horn clause analog Craven and Shavlik (1994) and Craven (1996) describe alternative methods for extracting symbolic rules from learned networks To understand the significance of KBANN it is useful to consider how its alhypothesis search differs from that of the purely inductive BACKPROPAGATION gorithm The hypothesis space search conducted by both algorithms is depicted schematically in Figure 124 As shown there, the key difference is the initial hypothesis from which weight tuning is performed In the case that multiple hypotheses (weight vectors) can be found that fit the data-a condition that will be especially likely when training data is scarce-KBANN is likely to converge to a hypothesis that generalizes beyond the data in a way that is more similar to the domain theory predictions On the other hand, the particular hypothesis to which BACKPROPAGATION converges will more likely be a hypothesis with small weights, corresponding roughly to a generalization bias of smoothly interpolating between training examples In brief, KBANN uses a domain-specific theory to bias generalization, whereas BACKPROPAGATIONa domain-independent syntactic bias uses toward small weight values Note in this summary we have ignored the effect of local minima on the search Limitations of KBANN include the fact that it can accommodate only propositional domain theories; that is, collections of variable-free Horn clauses It is also possible for KBANN to be misled when given highly inaccurate domain theories, so that its generalization accuracy can deteriorate below the level of BACKPROPAGATION Nevertheless, it and related algorithms have been shown to be useful for several practical problems KBANN illustrates the initialize-the-hypothesis approach to combining analytical and inductive learning Other examples of this approach include Fu (1993); Gallant (1988); Bradshaw et al (1989); Yang and Bhargava (1990); Lacher et al (1991) These approaches vary in the exact technique for constructing the initial. .net pdf 417 PDF - 417 C# Control - PDF - 417 barcode generator with free C# ...
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NET PDF-417 Generator SDK Tutorial tells users how to generate 2D PDF-417 Barcodes in .NET Framework with C# class. network, the application of BACKPROPAGATION to weight tuning, and in methods for extracting symbolic descriptions from the refined network Pratt (1993a, 1993b) describes an initialize-the-hypothesis approach in which the prior knowledge is provided by a previously learned neural network for a related task, rather than a manually provided symbolic domain theory Methods for training the values of Bayesian belief networks, as discussed in Section 611, can also be viewed as using prior knowledge to initialize the hypothesis Here the prior knowledge corresponds to a set of conditional independence assumptions that determine the graph structure of the Bayes net, whose conditional probability tables are then induced from the training data Listing 16-21 414,720 pels (720 576) 2997 (525/60), 25 (625/50) 2-bits/pel; 16-color/ 16-transparency CLUTs 720 720 480 (525/60), 576 (625/50) .net pdf 417 Best 20 NuGet pdf417 Packages - NuGet Must Haves Package
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PDF417 is a stacked linear barcode format used in a variety of applications such as transport, identification cards, and inventory management. "PDF" stands for ... The above approach begins the gradient descent search with a hypothesis that perfectly fits the domain theory, then perturbs this hypothesis as needed to maximize the fit to the training data An alternative way of using prior knowledge is to incorporate it into the error criterion minimized by gradient descent, so that the network must fit a combined function of the training data and domain theory In this section, we consider using prior knowledge in this fashion In particular, we consider prior knowledge in the form of known derivatives of the target function Certain types of prior knowledge can be expressed quite naturally in this form For example, in training a neural network to recognize handwritten characters we - (IBAction) deletePhoto : (id) sender { PhotosDAO * myPhotos = [[PhotosDAO alloc] init]; [myPhotos deletePhoto:currentID]; [selfphotos release]; selfphotos = [myPhotos getAllPhotos]; currentElement = 0; [myPhotos release]; } Still picture (I frame) Listing 16-22 704 704 .net pdf 417 2D barcode PDF417 library download | SourceForge. net
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