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While we have focused thus far on the PAC learning model, computational learning theory considers a variety of different settings and questions Different learning settings that have been studied vary by how the training examples are generated (eg, passive observation of random examples, active querying by the learner), noise in the data (eg, noisy or error-free), the definition of success (eg, the target concept must be learned exactly, or only probably and approximately), assumptions made by the learner (eg, regarding the distribution of instances and whether C G H), and the measure according to which the learner is evaluated (eg, number of training examples, number of mistakes, total time) In this section we consider the mistake bound model of learning, in which the learner is evaluated by the total number of mistakes it makes before it converges to the correct hypothesis As in the PAC setting, we assume the learner receives a sequence of training examples However, here we demand that upon receiving each example x, the learner must predict the target value c(x), before it is shown the correct target value by the trainer The question considered is "How many mistakes will the learner make in its predictions before it learns the target concept ' This question is significant in practical settings where learning must be done while the system is in actual use, rather than during some off-line training stage For example, if the system is to learn to predict which credit card purchases should be approved and which are fraudulent, based on data collected during use, then we are interested in minimizing the total number of mistakes it will make before converging to the correct target function Here the total number of mistakes can be even more important than the total number of training examples This mistake bound learning problem may be studied in various specific settings For example, we might count the number of mistakes made before PAC learning the target concept In the examples below, we consider instead the number of mistakes made before learning the target concept exactly Learning the target concept exactly means converging to a hypothesis such that (Vx)h(x) = c(x).

You replace function() with the name of the function you wish to repeat You then replace time with the time (in milliseconds) you want to wait before each repetition of the function So, if you really wanted to annoy your viewers, you could use this method to pop up an alert every 10 seconds (10,000 milliseconds) once the page is viewed You could do this by placing the following script inside the HTML of a document:

<body> <script type="text/javascript"> function annoy_alert() { windowalert("Am I bothering you yet "); } windowsetInterval("annoy_alert()",10000); </script> </body>

To illustrate, consider again the hypothesis space H consisting of conjunctions of up to n boolean literals 11 1, and their negations (eg, Rich A -Handsome) Recall the FIND-Salgorithm from 2, which incrementally computes the maximally specific hypothesis consistent with the training examples A straightforward implementation of FIND-Sfor the hypothesis space H is as follows:

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General Construction 457 Wire Sizes 460 Electrical Characteristics 460 Insulation 460 Color Coding and Marking 460 Shielding 461

Notice that this is a special case in which the function must be called inside quote marks Normally, a function must be called outside quote marks The reason for it here is to keep the function from executing immediately rather than at the set interval The function name is being sent as a string parameter to the setInterval() method, where it is then called after the correct time lapse This, of course, could become quite annoying The less time set in the interval, the more annoying it would become Luckily, the ten-second interval gives you enough time to leave the page before another alert pops up

The Subtract-and-Assign Operator ( =)

FIND-S:

The = operator works like the += operator, except that it subtracts the value on the right side of the operator from the variable on the left side This value is then assigned to the variable Here is an example of this operator in action:

Appendix B Legacy and Less Common Cabling Systems 463 Ethernet Coax 464 Thicknet (10Base5) Cabling 465 Thinnet (10Base2) Cabling 468 Ethernet Grounding 470 Coax Connectors 470 IBM Cabling System and Data Connectors 473 STP-A Data Connectors 473 ARCnet 475 Other Network Types 479 100VG-AnyLAN 479 Isochronous Ethernet 480 AppleTalk/LocalTalk 480 FDDI 481 ATM 482 Appendix C Online Resources 485 Appendix D Standards Organizations 487 Appendix E Standards for LAN Wiring 491 Appendix F Membership and Training Organizations 499

var mymoney=1000; var bills=800; mymoney-=bills;

Initialize h to the most specific hypothesis l1 A -II A 12 A -12 1, For each positive training instance x 0 Remove from h any literal that is not satisfied by x Output hypothesis h

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