barcode in vb.net 2010 MACHINE LEARNING in Software

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S 1 : {<Sunny, Warm, Normal, Strong, Warm, Same> }
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S2 : {<Sunny, Warm, , Strong, Warm, Same>}
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Training examples:
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1 <Sunny, Warm, Normal, Strong, Warm, Same>, Enjoy Sport = Yes 2 <Sunny, Warm, High, Strong, Warm, Same>, Enjoy Sport = Yes
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FIGURE 24 CANDIDATE-ELIMINATION So and Go are the initial boundary sets corresponding to the most Trace 1 specific and most general hypotheses Training examples 1 and 2 force the S boundary to become more general, as in the FIND-Salgorithm They have no effect on the G boundary
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two positive examples is very similar to the processing performed by the FIND-S algorithm As illustrated by these first two steps, positive training examples may force the S boundary of the version space to become increasingly general Negative training examples play the complimentary role of forcing the G boundary to become increasingly specific Consider the third training example, shown in Figure 25 This negative example reveals that the G boundary of the version space is overly general; that is, the hypothesis in G incorrectly predicts that this new example is a positive example The hypothesis in the G boundary must therefore be specialized until it correctly classifies this new negative example As shown in Figure 25, there are several alternative minimally more specific hypotheses All of these become members of the new G3 boundary set Given that there are six attributes that could be specified to specialize G2, why are there only three new hypotheses in G3 For example, the hypothesis h = ( , , Normal, , , ) is a minimal specialization of G2 that correctly labels the new example as a negative example, but it is not included in Gg The reason this hypothesis is excluded is that it is inconsistent with the previously encountered positive examples The algorithm determines this simply by noting that h is not more general than the current specific boundary, Sz In fact, the S boundary of the version space forms a summary of the previously encountered positive examples that can be used to determine whether any given hypothesis
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C H m R 2 CONCEPT LEARNING AND THE GENERAL-TO-SPECIFIC ORDERING
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s2 s 3 :
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( <Sunny, Wann, Strong, W a r n Same> ) ]
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G 3:
(<Sunny, , , , , > < , Wann, , , , > < , , , , , Same>}
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Training Example:
3 <Rainy, Cold, High, Strong, Warm, Change>, EnjoySporkNo
FIGURE 25
CANDIDATE-ELMNATION 2 Training example 3 is a negative example that forces the G2 Trace boundary to be specialized to G3Note several alternative maximally general hypotheses are included in Gj
is consistent with these examples Any hypothesis more general than S will, by definition, cover any example that S covers and thus will cover any past positive example In a dual fashion, the G boundary summarizes the information from previously encountered negative examples Any hypothesis more specific than G is assured to be consistent with past negative examples This is true because any such hypothesis, by definition, cannot cover examples that G does not cover The fourth training example, as shown in Figure 26, further generalizes the S boundary of the version space It also results in removing one member of the G boundary, because this member fails to cover the new positive example This last action results from the first step under the condition "If d is a positive example" in the algorithm shown in Table 25 To understand the rationale for this step, it is useful to consider why the offending hypothesis must be removed from G Notice it cannot be specialized, because specializing it would not make it cover the new example It also cannot be generalized, because by the definition of G, any more general hypothesis will cover at least one negative training example Therefore, the hypothesis must be dropped from the G boundary, thereby removing an entire branch of the partial ordering from the version space of hypotheses remaining under consideration After processing these four examples, the boundary sets S4 and G4 delimit the version space of all hypotheses consistent with the set of incrementally observed training examples The entire version space, including those hypotheses
S 3: {<Sunny, Warm, , Strong, Warm, Same>)
S 4:
( <Sunny, Warm , Strong, , >)
Training Example:
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