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EXPERIENCE INDIANA UNIVERSITY 1994 Assistant Professor of German, College of Arts and Sciences Taught advanced language courses and special topics in theories and history of language, aesthetics, literature, and mythology Team taught courses on computer assisted text analysis DEUTSCHE GESELLSCHAFT F R AUSW RTIGE POLITIK, e V Bonn, Federal Republic of Germany 1993 Senior Researcher Areas of interest: The European Community: Progress or Decline American foreign policy under change; the Middle East, and African Policy of the US since Carter Frankfurt, Federal Republic of Germany Bloomington, IN

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capture effects, which can be briefly engaging, but frankly there is no obvious application for this option. Well, that about wraps it up for Pixie. Small but well formed, a sadly neglected but very useful utility.

Inductive system Training examples New instance Candidate Elimination Using Hypothesis Space H Classification of new instance, or "don'tknow"

Equivalent deductive system I I Classification of new instance, or "don'tknow"

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Here is the scenario: I had a copy of the DailyJournal project stored on the network I took a copy off to my desktop and did some work on it but forgot to copy it back The next day I connected my notebook to the network and took a copy of that project to work on it I added an icon to the desktop copy, and an installation package document to the notebook copy, and made changes to the same class code file in both Oops I now have three copies of the same project, completely out of sync Oh, and I had neglected to read 11 of this book and so none of these folders was under Subversion source-code management What a mess.

Theorem Prover Assertion " Hcontains the target concept"

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FIGURE 28 Modeling inductive systems by equivalent deductive systems The input-output behavior of the CANDIDATE-ELIMINATION algorithm using a hypothesis space H is identical to that of a deductive theorem prover utilizing the assertion " H contains the target concept" This assertion is therefore called the inductive bias of the CANDIDATE-ELIMINATION algorithm Characterizing inductive systems by their inductive bias allows modeling them by their equivalent deductive systems This provides a way to compare inductive systems according to their policies for generalizing beyond the observed training data

I could work painstakingly through each folder to figure out the differences, but that s what computers are for, isn t it Let s see what FileMerge can do to help The first thing to do, in the interest of safety, was to copy all three folders somewhere where I could work on them For clarity I gave the three project folders different names: Network copy, Desktop copy, and Notebook copy Next I started up FileMerge This utility starts up with a small interface window: its default operation is to compare two files or folders, so that is what you see However, if you expand the window a little vertically, it pops open to show some additional options that suit what we want to do here; see Figure 13 10 NOTE: FileMerge is a bit of an oddity from a usability perspective.

theorem prover is given these same two inputs plus the assertion "H contains the target concept" These two systems will in principle produce identical outputs for every possible input set of training examples and every possible new instance in X Of course the inductive bias that is explicitly input to the theorem prover is algorithm In a sense, it only implicit in the code of the CANDIDATE-ELIMINATION exists only in the eye of us beholders Nevertheless, it is a perfectly well-defined set of assertions One advantage of viewing inductive inference systems in terms of their inductive bias is that it provides a nonprocedural means of characterizing their policy for generalizing beyond the observed data A second advantage is that it allows comparison of different learners according to the strength of the inductive bias they employ Consider, for example, the following three learning algorithms, which are listed from weakest to strongest bias

SKILLS Languages: German: Italian: French: Computer: Programming languages: Java; C; C++; COBOL; Virtual Basic Spread Sheet: Excel Fluent (speak, read, write, translate, interpret) Conversationally fluent Conversationally fluent

1 ROTE-LEARNER: Learning corresponds simply to storing each observed training example in memory Subsequent instances are classified by looking them

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