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Nunez (1988) describes a related approach and its application to learning medical diagnosis rules Here the attributes are different symptoms and laboratory tests with differing costs His system uses a somewhat different attribute selection measure 2GaWSA) - 1

There is one more thing to do before you build the package. The user interface provided by default, while pleasant, is not customized to your application. Let s do something about that. Click on the Edit Interface button at the top right. This gives access to the user interface design assistant (see Figure 15 15).

(Cost(A)

where w E [0, 11 is a constant that determines the relative importance of cost versus information gain Nunez (1991) presents an empirical comparison of these two approaches over a range of tasks

You can navigate through this in much the same way that you would if you were running the installation, using the Continue and Go Back buttons (I consider this assistant to be a fine example of a highly usable design for this and similar reasons). In this first section you will specify a different background. Recall back in 13 I created a 512 512pixel icon image for the DailyJournal. Well, thinking ahead, I also created a version of the same image with 20% opacity so that it would sit nicely in the background. If you have downloaded the files for this chapter, find the image called DJPackageMakerImage.png and drag it into the user interface. Set the scaling to To-Fit and your nice partly opaque image will sit across the background. Now move on to the Welcome Panel configuration. You can choose the text that the user sees by default this is You will be guided through the steps necessary to install this software. but you can write your own here or drop in a file with the text. If you create an RTF file in TextEdit you can include different fonts, weights, and colors. Let s stick with the standard. Move on to the Important Information section, and the same kinds of features are available. Try creating an RTF file in TextEdit and dropping it in here (see Figure 15 16):

The main points of this chapter include: Decision tree learning provides a practical method for concept learning and for learning other discrete-valued functions The ID3 family of algorithms infers decision trees by growing them from the root downward, greedily selecting the next best attribute for each new decision branch added to the tree ID3 searches a complete hypothesis space (ie, the space of decision trees can represent any discrete-valued function defined over discrete-valued instances) It thereby avoids the major difficulty associated with approaches that consider only restricted sets of hypotheses: that the target function might not be present in the hypothesis space The inductive bias implicit in ID3 includes a preference for smaller trees; that is, its search through the hypothesis space grows the tree only as large as needed in order to classify the available training examples Overfitting the training data is an important issue in decision tree learning Because the training examples are only a sample of all possible instances,

Research Assistant, Chemistry Department, Newcomb College Under Dr M P Norris, Spring 1996 Fall 1996 Experimented with synthesis of B-amino ketones via enol boronates, as they pertain to natural products Research Assistant, Chemistry Department, Newcomb College Under Dr M P Norris, 1995 1996 Experimented with SmI selective bond cleavage of carbon-oxygen single bonds Research Assistant, Chemistry Department, University of Tennessee Under Dr Craig Barnes, Summer 1994 Synthesis of macro-cycle containing two transition metal atoms Research Assistant, Chemistry Department, University of Tennessee Under Dr Craig Barnes, Spring 1994, Fall 1994 Synthesis of starting material for graduate student research