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NOTE: Strictly speaking, for an application as simple as this, you don t actually need to create a new controller. You can use the Hello_WorldAppDelegate object that Xcode has already created for you as your controller. Nevertheless, I m showing you here how to create your own, because this is something you will frequently need to do. This is not a book on Objective-C, but since most Cocoa software is written using Objective-C, I will need to cover the necessary code for you to create this application. I would recommend that you look at one of the great books on Objective-C and Cocoa in order to get the best grounding in the language. Let s think about what this application has to do. There is a button, and when it is clicked that button has to tell the application in some way that it has to write a string of text ( Hello, World! ) into the label. Cocoa accomplishes all this by having connections between the button and the controller, and between the label and the controller. The controller, sitting in the center, needs to know about more or less everything that is going on, and this is accomplished by message-passing between the objects. The flow of information in the application looks like Figure 3 14. Pressing the button sends a message to the controller initiating the sayHello: method. This method instructs the Text Field control to set its text a property called stringValue in Cocoa to a new value ( Hello, World! ).

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All that remains is to specify the learning algorithm for choosing the weights wi to^ best fit the set of training examples { ( b ,Vtrain(b))} a first step we must define As what we mean by the bestfit to the training data One common approach is to define the best hypothesis, or set of weights, as that which minimizes the squarg error E between the training values and the values predicted by the hypothesis V

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Thus, we seek the weights, or equivalently the c , that minimize E for the observed training examples 6 discusses settings in which minimizing the sum of squared errors is equivalent to finding the most probable hypothesis given the observed training data Several algorithms are known for finding weights of a linear function that minimize E defined in this way In our case, we require an algorithm that will incrementally refine the weights as new training examples become available and that will be robust to errors in these estimated training values One such algorithm is called the least mean squares, or LMS training rule For each observed training example it adjusts the weights a small amount in the direction that reduces the error on this training example As discussed in 4, this algorithm can be viewed as performing a stochastic gradient-descent search through the space of possible hypotheses (weight values) to minimize the squared enor E The LMS algorithm is defined as follows:

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sincere hope is that this book will help you past those learning curves, because once past them you will find Xcode an elegant, easy-to-use, and highly productive environment in which to work. Of course, there is one very good reason for making Xcode your development platform of choice the iPhone. At the time of writing, there simply is no other way to create iPhone software. If you are even thinking of writing iPhone apps, then you need to learn Xcode.

For each training example (b, Kmin(b))

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Here q is a small constant (eg, 01) that moderates the size of the weight update To get an intuitive understanding for why this weight update rule works, notice that when the error (Vtrain(b) c(b)) is zero, no weights are changed When (V,,ain(b)- e(b)) is positive (ie, when f ( b ) is too low), then each weight is increased in proportion to the value of its corresponding feature This will raise the value of (b), reducing the error Notice that if the value of some feature xi is zero, then its weight is not altered regardless of the error, so that the only weights updated are those whose features actually occur on the training example board Surprisingly, in certain settings this simple weight-tuning method can be proven to converge to the least squared error approximation to the &,in values (as discussed in 4)

One aim of this book is to dispel some of the confusion surrounding terminology The new developer has not only to learn a sophisticated and complex environment and toolset, but also a range of new concepts and terms In particular, the three terms Xcode, Cocoa, and Objective-C are often mixed up in the minds of newcomers to Mac development Let s nail this confusion right now Let s start with Cocoa This is the name given to the complete object-oriented environment within which you are working when you create software for Mac OS X and iPhone The most visible aspect of Cocoa is the collection of code libraries known as the Cocoa frameworks These provide you with a consistent and optimized set of prebuilt code modules that will vastly speed up your development process You ll learn a lot more about the Cocoa frameworks in 5.

The final design of our checkers learning system can be naturally described by four distinct program modules that represent the central components in many learning systems These four modules, summarized in Figure 11, are as follows:

The Performance System is the module that must solve the given performance task, in this case playing checkers, by using the learned target function(s) It takes an instance of a new problem (new game) as input and produces a trace of its solution (game history) as output In our case, the

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