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explained by the domain theory and de-emphasized for training examples that are poorly explained The inputs to EBNN include (1) a set of training examples of the form (xi,f (xi))with no training derivatives provided, and (2) a domain theory analogous to that used in explanation-based learning ( 11) and in KBANN, but represented by a set of previously trained neural networks rather than a set of Horn clauses The output of EBNN is a new neural network that approximates the target function f This learned network is trained to fit both the training examples (xi, (xi))and training derivatives of f extracted from the domain theory Fitting f f the training examples (xi, (xi))constitutes the inductive component of learning, whereas fitting the training derivatives extracted from the domain theory provides the analytical component To illustrate the type of domain theory used by EBNN, consider Figure 127 The top portion of this figure depicts an EBNN domain theory for the target function Cup, with each rectangular block representing a distinct neural network in the domain theory Notice in this example there is one network for each of the Horn clauses in the symbolic domain theory of Table 123 For example, the network labeled Graspable takes as input the description of an instance and produces as output a value indicating whether the object is graspable (EBNN typically represents true propositions by the value 08 and false propositions by the value 02) This network is analogous to the Horn clause for Graspable given in Table 123 Some networks take the outputs of other networks as their inputs (eg, the rightmost network labeled Cup takes its inputs from the outputs of the Stable, Lifable, and OpenVessel networks) Thus, the networks that make up the domain theory can be chained together to infer the target function value for the input instance, just as Horn clauses might be chained together for this purpose In general, these domain theory networks may be provided to the learner by some external source, or they may be the result of previous learning by the same system EBNN makes use of these domain theory networks to learn the new,target function It does not alter the domain theory networks during this process The goal of EBNN is to learn a new neural network to describe the target function We will refer to this new network as the target network In the example of Figure 127, the target network Cup,,,,,, shown at the bottom of the figure takes as input the description of an arbitrary instance and outputs a value indicating whether the object is a Cup EBNN learns the target network by invoking the TANGENTPROP algorithm described in the previous section Recall that TANGENTPROP a network to fit trains both training values and training derivatives EBNN passes along to TANGENTPROP f the training values (xi, (xi))that it receives as input In addition, EBNN provides TANGENTPROP derivatives that it calculates from the domain theory To see with how EBNN calculates these training derivatives, consider again Figure 127 The top portion of this figure shows the domain theory prediction of the target function value for a particular training instance, xi EBNN calculates the derivative of this prediction with respect to each feature of the input instance For the example in the figure, the instance xi is described by features such as MadeOf Styrof oam = 02.

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bumps the playing time up to around one hour A DVD-Video can easily hold a 4-hour movie on one side Audio DVD-Video has up to eight digital audio tracks Each track can hold uncompressed audio with quality better than audio CD or compressed 51-channel surround sound in Dolby Digital or MPEG-2 format SVCD has one or two tracks of compressed 2-channel audio in MPEG-1 Layer II format or one track of MPEG 51-channel audio Video The quality of DVD-Video exceeds that of SVCD, with one and a half times the resolution in normal mode and two times in widescreen mode (compared with letterboxed SVCD) DVD uses an average MPEG-2 data rate that is two or three times higher than SVCD

Relationship check box Ensure the Optional check box is selected and the Inverse says No Inverse Relationship Change the Delete Rule to Cascade (Figure 17-9)

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Price Currently, SVCD has a price advantage over DVD SVCD movies sell for as little as $3 in Asian countries, although generally they are not available outside Asia SVCD players typically cost $150 to $250, compared with DVD players at $150 to $500 DVD-Video player prices will drop as its market becomes bigger than the SVCD market, especially since DVD players do not require three-disc mechanisms

Explanation of training example in terms of domain theory:

Name to crates and select the To-Many Relationship check box Change the Delete Rule to Cascade Ensure the Optional check box is selected and the Inverse is No Inverse Relationship

A few DVD-Video players, particularly those made in Asian countries, can play SVCDs because the DVD player already has MPEG capability Fast Pentium II or Mac G3 computers can play SVCDs as long as they have the necessary software

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After creating the relationships between FruitStand and Crate, and Crate and Fruit, there are two build warnings stating that there isn t an inverse relationship between those same entities What that means is that in the case of the crate-to-fruit relationship, not only should you say that one crate can hold multiple fruit, but that one fruit can only be in one crate at a time For simplicity, ignore this inverse relationship, but realize that in reality, these warnings are both correct: a crate can only be in one FruitStand and a Fruit can only be in one Crate

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Many variations of the basic CD standard have been created to address limitations of the original design or to add new features Most of these features have been integrated into DVD DVD-ROM drives and DVD-Video players generally are backward-compatible with common mutations of CD The details follow:

17:

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Notice you specified all relationships have a Cascade Delete Rule This rule informs the model that when an object containing a reference to another object is deleted, delete the referenced object For instance, FruitStand references one or more Crate entities The relationship is cascade, and so when you delete a FruitStand, the context deletes all the stand s referenced crates Other delete rules you might specify include Nullify, Deny, and No Action

DVD drives and players usually can read rewritable CDs (Orange Book Part III) CD-RW discs do not work in CD-ROM readers manufactured before 1997, and many CD audio players still do not read CD-RW discs CD-RW has a lower reflectivity difference than specified by the CD standard, thus requiring automatic gain control (AGC) circuitry Since DVDROM drives and DVD players already contain AGC, they are usually able to read CD-RW discs because the CD-RW format does not have the invisibility problem of CD-R (see the preceding section) The OSTA MultiRead logo guarantees compatibility with CD-R and CDRW, but it is not widely used, even on hardware that is compatible

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For more information on creating models using Xcode, refer to Apple s Creating a Managed Object Model with Xcode

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