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To illustrate the practical importance of Bayesian learning methods, consider learning problems in which the instances are text documents For example, we might wish to learn the target concept "electronic news articles that I find interesting," or "pages on the World Wide Web that discuss machine learning topics" In both cases, if a computer could learn the target concept accurately, it could automatically filter the large volume of online text documents to present only the most relevant documents to the user We present here a general algorithm for learning to classify text, based on the naive Bayes classifier Interestingly, probabilistic approaches such as the one described here are among the most effective algorithms currently known for learning to classify text documents Examples of such systems are described by Lewis (1991), Lang (1995), and Joachims (1996) The naive Bayes algorithm that we shall present applies in the following general setting Consider an instance space X consisting of all possible text documents (ie, all possible strings of words and punctuation of all possible lengths) We are given training examples of some unknown target function f ( x ) , which can take on any value from some finite set V The task is to learn from these training examples to predict the target value for subsequent text documents For illustration, we will consider the target function classifying documents as interesting or uninteresting to a particular person, using the target values like and dislike to indicate these two classes The two main design issues involved in applying the naive Bayes classifier to such rext classification problems are first to decide how to represent an arbitrary text document in terms of attribute values, and second to decide how to estimate the probabilities required by the naive Bayes classifier Our approach to representing arbitrary text documents is disturbingly simple: Given a text document, such as this paragraph, we define an attribute for each word position in the document and define the value of that attribute to be the English word found in that position Thus, the current paragraph would be described by 111 attribute values, corresponding to the 111 word positions The value of the first attribute is the word "our," the value of the second attribute is the word "approach," and so on Notice that long text documents will require a larger number of attributes than short documents As we shall see, this will not cause us any trouble.

You are going to create a screen saver that shows the famous OpenGL teapot When it is complete the screen saver will look like this (see Figure 5 12) You start in the by now familiar way, although unlike most Cocoa projects you are not going to use Interface Builder in this example In Xcode, choose a new project This time choose the System Plug-in item in the User templates list and select Screen Saver Give the project the name TumblingTeapot You will see the usual Xcode project window, but notice the ScreenSaverframework item in the detail list Take a look at the structure of your class (TumblingTeapotm) All of the basic routines for your screensaver are already there In fact, this is already a working screensaver If you really wanted, you could build this and install it.

Given this representation for text documents, we can now apply the naive Bayes classifier For the sake of concreteness, let us assume we are given a set of 700 training documents that a friend has classified as dislike and another 300 she has classified as like We are now given a new document and asked to classify it Again, for concreteness let us assume the new text document is the preceding paragraph In this case, we instantiate Equation (620) to calculate the naive Bayes classification as Vns = argmax P(Vj) ~ ( alvj) i i=l P(vj) P(a1 = "our"lvj)P(a2 = "approach"lvj)

vj~{like,dislike}

PROFESSIONAL OBJECTIVE To obtain a position as advisor to Governor Nancy Lee (R-LA) regarding women s issues

Of course, all it would do is give you a blank screen, but if you should ever want a simple blank screen saver, this is one way to do it!.

- a -

- argmax -

Table 5 4. Methods Provided in the Default Screen Saver Project initWithFrame startAnimation stopAnimation drawRect animateOneFrame This is where any initialization needs to happen. Anything that needs to happen at the beginning of the animation stage can go in here. Likewise, anything that happens at the end of the animation goes here. This is the main drawing routine. All of the code that delivers your images, if it is anything more than really trivial, should go here. This controls the animation process for the screen saver. In this example, it is simply setting up a motion and asking the display to redraw. If your screen saver has configuration parameters, set the return value here to true. If you have set hasConfigureSheet to YES then you need to code the loading of the configuration sheet here.

v, ~{like,dislike}

.

The main work to do here is to add code to the appropriate methods, as well as to create a few extra utility methods.