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positive examples and few of the negative examples We require that this iaarput rule have high accuracy, but not necessarily high coverage By high accuracy, we mean the predictions it makes should be correct By accepting low coverage, we mean it need not make predictions for every training example Given this LEARN-ONE-RULE subroutine for learning a single rule, one obvious approach to learning a set of rules is to invoke LEARN-ONE-RULE the on all available training examples, remove any positive examples covered by the rule it learns, then invoke it again to learn a second rule based on the remaining training examples This procedure can be iterated as many times as desired to learn a disjunctive set of rules that together cover any desired fraction of the positive examples This is called a sequential covering algorithm because it sequentially learns a set of rules that together cover the full set of positive examples The final set of rules can then be sorted so that more accurate rules will be considered first when a new instance must be classified A prototypical sequential covering algorithm is described in Table 101 This sequential covering algorithm is one of the most widespread approaches to learning disjunctive sets of rules It reduces the problem of learning a disjunctive set of rules to a sequence of simpler problems, each requiring that a single conjunctive rule be learned Because it performs a greedy search, formulating a sequence of rules without backtracking, it is not guaranteed to find the smallest or best set of rules that cover the training examples How shall we design LEARN-ONE-RULE to meet the needs of the sequential covering algorithm We require an algorithm that can formulate a single rule with high accuracy, but that need not cover all of the positive examples In this section we present a variety of algorithms and describe the main variations that have been explored in the research literature In this section we consider learning only propositional rules In later sections, we extend these algorithms to learn first-order Horn clauses.

PROFESSIONAL AFFILIATIONS American Psychological Association, Division 29 Minnesota Women Psychologists Society for Music Therapists PUBLICATIONS and PRESENTATIONS Allen, B, Weinstein, F, Schmidt, G (1993) Effects of stress on creativity among teens who achieve perfect scores on SATs Educational & Psychological Measurement, 100, 1108 1121 Davis, M, Schmidt, G, & Musa, M (1992, June) Psychotherapy and High Achievers: Research Guidelines Included in proceedings at Annual Conference of Minnesota Women Psychologists Schmidt, G, & Cohen, A (1991, September) Mood and Memory: Evaluating the Psychological Functioning of Child Prodigies Paper presented at Symposium on Creativity, Psychology Department, Stanford University, Palo Alto, CA REFERENCES Available upon request

S E Q U E N T I A L - C O V E R I N G ( T ~ ~ ~ ~Examples, Threshold), Attributes, ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

The Mini Debugger provides a small floating HUD-style window that gives access to the main debugging features with the smallest screen footprint. Open up the TemperatureConverter project if it is not already open. Put the breakpoints in as you had them earlier (refer to Figure 7 13). Now Build and Debug. Once the program has started to run, choose the Run Mini Debugger menu. You will see the small HUD window (see Figure 7 15).

0 0 0

Learnedxules c {} Rule c ~ ~ ~ ~ ~ - o ~ ~ - ~ ~ ~ ~ ( T a r g eAttributes, Examples) tattribute,

It may not look like it is doing very much. Enter a new Centigrade temperature value. The result should look like Figure 7 16. The HUD display zooms up to a larger window to show the point of execution.

while P E R F O R M A N C E ( Examples) > Threshold, do RU~~,

L e a r n e d ~ u l e s Learnedxules Rule c Examples c Examples - {examples correctly classified by Rule] 0 Rule c ~ ~ ~ ~ ~ - o N ~ - R u L ~ ( T a r g e t l l t tAttributes, Examples) ribute, Learnedxules c sort Learned-rules accord to PERFORMANCE over Examples return Learnedxules

When you use one of the controls on the ribbon menu to continue past the breakpoint, the window zooms back down to the minimal view. The nice thing about the Mini Debugger view is that it is compact: if you are limited on screen space it can be a useful way to manage your debugging; however, it has some drawbacks. For one thing, code is not editable in the HUD code view. The continual zooming of windows can also be a little confusing. To be honest, I have never used the Mini Debugger and see little advantage over the standard debugger display.

Software Development Extensive knowledge of C; C++, and COBOL; data communications/networking; TCP/IP; Ethernet; database and design support software; fault tolerant computing; RISC technology; UNIX; VSAT; ATM; Windows NT Hardware Development Experienced in design, testing, quality assurance, systems integration, reliability, and field engineering of computer systems and subsystems that include CPUs, memory systems, power supplies, power distribution systems, storage devices (tape disks and optical devices), and storage subsystems (device, power, packaging, and controllers) High speed ECL, CMOS, and RISC technologies, which utilize state-of-the-art CAD and CAE design Networks OSI; LAN; CICS;, NOVEL; ISDN; frame relay; satellite multiple access techniques; terrestrial networking interfacing

TABLE 101 The sequential covering algorithm for learning a disjunctive set of rules LEARN-ONE-RULE must return a single rule that covers at least some of the Examples PERFORMANCE is a user-provided subroutine to evaluate rule quality This covering algorithm learns rules until it can no longer learn a rule whose performance is above the given Threshold