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Double-click this file and you will see a message asking whether you want to install the screen saver just for you or for all users. Choose one of the options (it doesn t really matter which it s probably easier to install it just for you since you then don t need to authenticate as an admin user) and click Install.

The most basic instance-based method is the k-NEAREST NEIGHBOR algorithm This algorithm assumes all instances correspond to points in the n-dimensional space 8" The nearest neighbors of an instance are defined in terms of the standard

where ar ( x ) denotes the value of the rth attribute of instance x Then the distance between two instances xi and xj is defined to be d ( x i ,x j ) , where

Summary

In nearest-neighbor learning the target function may be either discrete-valued or real-valued Let us first consider learning discrete-valued target functions of the form f : W -+ V, where V is the finite set {vl, v,} The k-NEAREST NEIGHBOR algorithm for approximatin5 a discrete-valued target function is given in Table 81 As shown there, the value f (x,) returned by this algorithm as its estimate of f (x,) is just the most common value of f among the k training examples nearest to x, If we choose k = 1, then the 1-NEAREST NEIGHBOR algorithm assigns to f ( x , ) the value f ( x i ) where xi is the training instance nearest to x, For larger values of k, the algorithm assigns the most common value among the k nearest training examples Figure 81 illustrates the operation of the k-NEAREST NEIGHBOR algorithm for the case where the instances are points in a two-dimensional space and where the target function is boolean valued The positive and negative training examples are shown by "+" and "-" respectively A query point x, is shown as well Note the 1-NEAREST NEIGHBOR algorithm classifies x, as a positive example in this figure, whereas the 5-NEAREST NEIGHBOR algorithm classifies it as a negative example What is the nature of the hypothesis space H implicitly considered by the k-NEAREST NEIGHBOR algorithm Note the k-NEAREST NEIGHBOR algorithm never forms an explicit general hypothesis f regarding the target function f It simply computes the classification of each new query instance as needed Nevertheless,

Training algorithm: For each training example ( x , f ( x ) ) ,add the example to the list trainingaxamples Classification algorithm: Given a query instance xq to be classified, Let xl xk denote the k instances from trainingaxamples that are nearest to xq Return

THE NGAMI MFUMBIRO FOUNDATION Lagos, Nigeria, and Washington, DC Manage a by-invitation coterie of African, Middle Eastern, and Near Eastern countries, which support economic, educational, and scientific development in their respective countries; supervise staff of 75 consultants in Lagos office and 200 in Washington; manage annual budget of $50 million; report to Board of Advisors composed of representatives of participating countries and funding areas

You ve achieved a lot in this chapter. By now you should be very comfortable working with Interface Builder, and understanding how actions, connections, and bindings work. You have grappled with some of the most important Cocoa frameworks and have written some neat applications into the bargain. In 6, you are going to take a closer look at Model View Controller, the design principle that underlies most of the development work you will do in Cocoa. You will see a couple of examples that I hope will cement this architectural pattern for you (remember, this is one of those major barriers to understanding that you need to overcome to get a good working relationship with Cocoa). If you have ever seen this phrase, or its acronym MVC, batted around and felt that you weren t quite sure what it meant, this next chapter is for you.

TABLE 81 The k-NEAREST NEIGHBOR algorithm for approximating a discrete-valued function f : 8" -+ V

FIGURE 81 k-NEAREST NEIGHBOR set of positive and negative training examples is shown on the left, along A NEIGHBOR algorithm classifies x, positive, with a query instance x, to be classified The I-NEAREST whereas 5-NEAREST NEIGHBOR classifies it as negative On the right is the decision surface induced by the 1-NEAREST NEIGHBOR algorithm for a typical set of training examples The convex polygon surrounding each training example indicates the region of instance space closest to that point (ie, the instances for which the 1-NEAREST NEIGHBOR algorithm will assign the classification belonging to that training example)