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Learning to recognize spoken words All of the most successful speech recognition systems employ machine learning in some form For example, the SPHINX system (eg, Lee 1989) learns speaker-specific strategies for recognizing the primitive sounds (phonemes) and words from the observed speech signal Neural network learning methods (eg, Waibel et al 1989) and methods for learning hidden Markov models (eg, Lee 1989) are effective for automatically customizing to,individual speakers, vocabularies, microphone characteristics, background noise, etc Similar techniques have potential applications in many signal-interpretation problems Learning to drive an autonomous vehicle Machine learning methods have been used to train computer-controlled vehicles to steer correctly when driving on a variety of road types For example, the ALVINN system (Pomerleau 1989) has used its learned strategies to drive unassisted at 70 miles per hour for 90 miles on public highways among other cars Similar techniques have possible applications in many sensor-based control problems Learning to classify new astronomical structures Machine learning methods have been applied to a variety of large databases to learn general regularities implicit in the data For example, decision tree learning algorithms have been used by NASA to learn how to classify celestial objects from the second Palomar Observatory Sky Survey (Fayyad et al 1995) This system is now used to automatically classify all objects in the Sky Survey, which consists of three terrabytes of image data Learning to play world-class backgammon The most successful computer programs for playing games such as backgammon are based on machiie learning algorithms For example, the world's top computer program for backgammon, TD-GAMMON (Tesauro 1992, 1995) learned its strategy by playing over one million practice games against itself It now plays at a level competitive with the human world champion Similar techniques have applications in many practical problems where very large search spaces must be examined efficiently.

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EDUCATION, cont d 1984 THE UNIVERSITY OF CALGARY BSc (Honors) in psychology Concentration: Minor: SKILLS Languages: Fluent in oral and written French, Spanish, and Portuguese Conversant in oral German Knowledge of UNIX; DOS environment; C Language; C++; LAN/WAN Communication protocols ATM; VSAT; ISDN Experimental Psychology Statistics Calgary, Alberta

That s about it, really. BigTop is a useful tool to keep running while you are testing, particularly if you think your software is performing a little sluggishly. It s worth mentioning that many of the features are also available within Instruments.

three features: the class of tasks, the measure of performance to be improved, and the source of experience

A checkers learning problem:

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The final tool I ll describe in this chapter is Spin Control. It has just one function to look out for conditions in running programs in which a hang occurs. It then gives an analysis which in principle allows you to see what is going wrong in your application. Once again, you need to modify the startCalculation method in Fibonacci Fun. To give us a good chance of getting a beachball (the spinning nonresponsive cursor state familiar to most Mac users), raise the number of iterations around the outer loop again, this time to 200. Then Build and Run but don t press the Run calculation button yet. Find Spin Control in /Developer/Applications/Performance Tools and start it up. The interface is unassuming (see Figure 12 20):

Task T: playing checkers Performance measure P: percent of games won against opponents Training experience E: playing practice games against itself

We can specify many learning problems in this fashion, such as learning to recognize handwritten words, or learning to drive a robotic automobile autonomously

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Once both programs are running (position them side by side so you can monitor them), click the Run calculation button. All being well, after a few seconds you should see a beachball. Straight away Spin Control will show the message Fibonacci Fun is unresponsive. Some time later, the calculation will finish and the Spin Control message will change to Fibonacci Fun is responsive again. (See Figure 12 21.)

A handwriting recognition learning problem:

Telecommunications:

Task T: recognizing and classifying handwritten words within images Performance measure P : percent of words correctly classified

From this you can get two reports. One is a text report. This shows a hierarchical call trace, but I don t find this a very helpful view as a developer. It probably is useful in the event of having to submit a bug report for software that you are testing. The other report is the Call Graph View. To get this, select the Sample report line in the Detected Hangs

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