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where c is the number of distinct values the target function may take on, and where pi is the proportion of examples from S for which the target function takes on the ith value This entropy measure, combined with a test for statistical significance, is used in the CN2 algorithm of Clark and Niblett (1989) It is also the basis for the information gain measure used by many decision tree learning algorithms

This gives a detailed description of the Cocoa Frameworks (which you covered to some extent back in 5). It also provides links to more advanced topics such as The Objective-C Programming Language, Cocoa Design Patterns, and Garbage Collection Programming (see Figure 8-3).

I Consultant, 1991 1993 Worldwide Information Services (WWIS), Unisys Corporation, Bismarck, ND Advise clients in creative use and application of technology to improve service to customers, enhance their competitive position in their marketplace, and increase their flexibility I Visiting Assistant Professor, 1990, Department of Computer Science, University of Hawaii, Honolulu, HI Taught graduate courses in artificial intelligence, algorithms and theory, constructive logic, and computer graphics

(ie, variable-free) rules In this section, we consider learning rules that contain variables-in particular, learning first-order Horn theories Our motivation for considering such rules is that they are much more expressive than propositional rules Inductive learning of first-order rules or theories is often referred to as inductive logic programming (or L P for short), because this process can be viewed as automatically inferring PROLOG programs from examples PROLOG is a general purpose, Turing-equivalent programming language in which programs are expressed as collections of Horn clauses

To see the advantages of first-order representations over propositional (variablefree) representations, consider the task of learning the simple target concept Daughter ( x , y), defined over pairs of people x and y The value of Daughter(x, y ) is True when x is the daughter of y, and False otherwise Suppose each person in the data is described by the attributes Name, Mother, Father, Male, Female Hence, each training example will consist of the description of two people in terms of these attributes, along with the value of the target attribute Daughter For example, the following is a positive example in which Sharon is the daughter of Bob:

NOTE: Many of the documents you encounter in the documentation are also available as PDF files. Just click the PDF icon link, either in the title bar (see Figure 8-3) or above the Table of Contents in the top-left corner of the document. These can be very useful if you prefer to work with paper. Also, note that some of the documents are online, so when you are without a network connection, the PDF is the only option (provided that you have already downloaded it, of course!).

(Namel = Sharon, Malel = False, Name2 = Bob, Male2 = True, Motherl = Louise, Female1 = True, Mother2 = Nora, Female2 = False, Fatherl = Bob, Father2 = Victor, Daughterl2 = T r u e )

where the subscript on each attribute name indicates which of thetwo persons is being described Now if we were to collect a number of such training examples for the target concept Daughterlv2and provide them to a propositional rule learner such as CN2 or C45, the result would be a collection of very specific rules such as

Follow these pages on the ADC website, to get some basic Xcode programming tutorials. While these are useful, if you have followed the examples in earlier chapters you are already way past these in your learning!

Although it is correct, this rule is so specific that it will rarely, if ever, be useful in classifying future pairs of people The problem is that propositional representations offer no general way to describe the essential relations among the values of the attributes In contrast, a program using first-order representations could learn the following general rule:

Father(y, x )