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(Figure 9-21) Change the button s title to Next The button should automatically be set as the navigation item s rightBarButtonItem If not, connect the navigation item s rightBarButton outlet to the newly added bar button item

20:1

(continued)

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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9:

(game history)

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Delegate protocol Also add a UITabBarController property and a UINavigationController property Modify applicationDidFinishLaunching so it loads the tabBarController property s root view The files should match Listings 9-11 and 9-12 Save and build Listing 9-11 NavInTabAppDelegateh

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strategy used by the Performance System to select its next move at each step is determined by the learned p evaluation function Therefore, we expect its performance to improve as this evaluation function becomes increasingly accurate e The Critic takes as input the history or trace of the game and produces as output a set of training examples of the target function As shown in the diagram, each training example in this case corresponds to some game state of in the trace, along with an estimate Vtrai, the target function value for this example In our example, the Critic corresponds to the training rule given by Equation (11) The Generalizer takes as input the training examples and produces an output hypothesis that is its estimate of the target function It generalizes from the specific training examples, hypothesizing a general function that covers these examples and other cases beyond the training examples In our example, the Generalizer corresponds to the LMS algorithm, and the output hypothesis is the function f described by the learned weights wo, , W6 The Experiment Generator takes as input the current hypothesis (currently learned function) and outputs a new problem (ie, initial board state) for the Performance System to explore Its role is to pick new practice problems that will maximize the learning rate of the overall system In our example, the Experiment Generator follows a very simple strategy: It always proposes the same initial game board to begin a new game More sophisticated strategies

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#import<UIKit/UIKith> @interface NavInTabAppDelegate : NSObject<UIApplicationDelegate, UITabBarControllerDelegate> { UIWindow *window; UITabBarController *tabBarController; UINavigationController *navBarController; } @property (nonatomic, retain) IBOutlet UIWindow *window; @property (nonatomic, retain) IBOutlet UITabBarController *tabBarController; @property (nonatomic, retain) IBOutlet UINavigationController * navBarController; @end

36:1

(continued)

could involve creating board positions designed to explore particular regions of the state space Together, the design choices we made for our checkers program produce specific instantiations for the performance system, critic; generalizer, and experiment generator Many machine learning systems can-be usefully characterized in terms of these four generic modules The sequence of design choices made for the checkers program is summarized in Figure 12 These design choices have constrained the learning task in a number of ways We have restricted the type of knowledge that can be acquired to a single linear evaluation function Furthermore, we have constrained this evaluation function to depend on only the six specific board features provided If the true target function V can indeed be represented by a linear combination of these

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Listing 9-12

Main points of this chapter include:

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#import "NavInTabAppDelegateh" @implementation NavInTabAppDelegate @synthesize window; @synthesize tabBarController; @synthesize navBarController; - (void)applicationDidFinishLaunching:(UIApplication *)application { [window addSubview:tabBarControllerview]; [window makeKeyAndVisible]; } - (void)dealloc { [window release]; [tabBarController release]; [navBarController release]; [super dealloc]; } @end 11 Open MainWindowxib in Interface Builder and connect the NavInTabAppDelegate s

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Artificial neural network learning provides a practical method for learning real-valued and vector-valued functions over continuous and discrete-valued attributes, in a way that is robust to noise in the training data The BACKPROPAGATION algorithm is the most common network learning method and has been successfully applied to a variety of learning tasks, such as handwriting recognition and robot control The hypothesis space considered by the BACKPROPAGATION algorithm is the space of all functions that can be represented by assigning weights to the given, fixed network of interconnected units Feedforward networks containing three layers of units are able to approximate any function to arbitrary accuracy, given a sufficient (potentially very large) number of units in each layer Even networks of practical size are capable of representing a rich space of highly nonlinear functions, making feedforward networks a good choice for learning discrete and continuous functions whose general form is unknown in advance

navBarController to the newly added navigation controller (Figure 9-22) Connect the tabBarController to the newly added tab bar controller

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