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This popping open behavior, for example: why didn t the developer use a disclosure arrow, or More/Less buttons, either of which would be more Mac-like FileMerge also uses a drop-down list to launch a new window where you would normally expect just to make a choice between options in the same window To me this breaks the cardinal usability rule of not surprising your user We ll be returning to the topic of usability in a lot more detail in the next chapter FileMerge allows you to compare the contents of two child folders against a common original folder (called an ancestor) I entered the location of the Network copy as the ancestor, and the Notebook and Desktop copies as respectively the Left and Right derived versions.

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up in memory If the instance is found in memory, the stored classification is returned Otherwise, the system refuses to classify the new instance 2 CANDIDATE-ELIMINATION algorithm: New instances are classified only in the case where all members of the current version space agree on the classification Otherwise, the system refuses to classify the new instance 3 FIND-S:This algorithm, described earlier, finds the most specific hypothesis consistent with the training examples It then uses this hypothesis to classify all subsequent instances The ROTE-LEARNER no inductive bias The classifications it provides has for new instances follow deductively from the observed training examples, with no additional assumptions required The CANDIDATE-ELIMINATION algorithm has a stronger inductive bias: that the target concept can be represented in its hypothesis space Because it has a stronger bias, it will classify some instances that the ROTELEARNER not Of course the correctness of such classifications will depend will completely on the correctness of this inductive bias The FIND-Salgorithm has an even stronger inductive bias In addition to the assumption that the target concept can be described in its hypothesis space, it has an additional inductive bias assumption: that all instances are negative instances unless the opposite is entailed by its other know1edget As we examine other inductive inference methods, it is useful to keep in mind this means of characterizing them and the strength of their inductive bias More strongly biased methods make more inductive leaps, classifying a greater proportion of unseen instances Some inductive biases correspond to categorical assumptions that completely rule out certain concepts, such as the bias "the hypothesis space H includes the target concept" Other inductive biases merely rank order the hypotheses by stating preferences such as "more specific hypotheses are preferred over more general hypotheses" Some biases are implicit in the learner and are unchangeable by the learner, such as the ones we have considered here In s 11 and 12 we will see other systems whose bias is made explicit as a set of assertions represented and manipulated by the learner.

The use of Left and Right as descriptions here is quite arbitrary, by the way (another usability flaw): they are flags that identify the two versions that you want to compare These also identify the two panels in the comparison view, which you will see a little later The final thing to consider was what to do once I had figured out where the most relevant files belonged FileMerge has the option of a merge folder that holds the correct.

The main points of this chapter include: Concept learning can be cast as a problem of searching through a large predefined space of potential hypotheses The general-to-specific partial ordering of hypotheses, which can be defined for any concept learning problem, provides a useful structure for organizing the search through the hypothesis space

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version of each file as the result of the compare operation. I created a new folder called Fixed copy. The final FileMerge setup looked like Figure 13 10: