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PUBLICATIONS AND PRESENTATIONS Jackson, Acy L, and C Kathleen Geckeis How to Prepare Your Curriculum Vitae, with McGraw-Hill for publication in Spring 2003 Assessing Factors That Influence the Content of Conference Reports: Stating Clear Objectives and Outcomes Presentation at the International Writing Centers Association Conference in Savannah, GA April 11, 2002 COMMUNICATION SKILLS LANGUAGES: Fluent English German

asserts that one is a Student if at least two of these three preconditions are satisfied Give an algorithm similar to that used by KBANN, that accepts a set of propositional n-of-m clauses and constructs a neural network consistent with the domain theory 123 Consider extending KBANN to accept a domain theory consisting of first-order rather than propositional Horn clauses (ie, Horn clauses containing variables, as in 10) Either give an algorithm for constructing a neural network equivalent to a set of Horn clauses, or discuss the difficulties that prevent this 124 This exercise asks you to derive a gradient descent rule analogous to that used by TANGENTPROP Consider the instance space X consisting of the real numbers, and consider the hypothesis space H consisting of quadratic functions of x That is,

( a ) Derive a gradient descent rule that minimizes the same criterion as BACKPROP-

As you can see, it s not terribly informative apart from confirming that the test has passed (see the circled section in Figure 11 5). Later in the chapter, you will see how to get some more detailed information. However, it confirms that your basic algorithm is working for a normal case. Now let s try writing an assertion for something that we know is wrong. Let s test to see whether the algorithm knows that 100 C is different from 100 F. Add the test method shown in Listing 11 3 to TCTests.m below the first test method.

AGATION; is, the sum of squared errors between the hypothesis and target that values of the training data (b) Derive a second gradient descent rule that minimizes the same criterion as TANGENTPROP Consider only the single transformation s ( a , x ) = x + a 125 EBNN extracts training derivatives from explanations by examining the weights and activations of the neural networks that make up the explanation Consider the simple example in which the explanation is formed by a single sigmoid unit with n inputs Derive a procedure for extracting the derivative where xi is a particular training instance input to the unit, f ( x ) is the sigmoid unit output, and xi denotes the jth input to the sigmoid unit You may wish to use the notation x! to refer to the jth component of xi Hint: The derivation is similar to the derivation of the BACKPROPAGATION rule training 126 Consider again the search trace of FOCL shown in Figure 128 Suppose that the hypothesis selected at the first level in the search is changed to

Describe the second-level candidate hypotheses that will be generated by FOCL as successors to this hypothesis You need only include those hypotheses generated by FOCL's second search operator, which uses its domain theory Don't forget to post-prune the sufficient conditions Use the training data from Table 123 127 This chapter discussed three approaches to using prior knowledge to impact the search through the space of possible hypotheses Discuss your ideas for how these three approaches could be integrated Can you propose a specific algorithm that integrates at least two of these three for some specific hypothesis representation What advantages and disadvantages would you anticipate from this integration 128 Consider again the question from Section 1221, regarding what criterion to use for choosing among hypotheses when both data and prior knowledge are available Give your own viewpoint on this issue

Listing 11 3. Adding a Test Designed to Fail - (void)test100CShouldNotConvertTo100F { testTempConverter = Converter.new; [testTempConverter setValue:[NSNumber numberWithFloat:100.0] forKey:@"originalTemp"]; NSNumber *newTemperatureInF = [NSNumber numberWithFloat:(float)[testTempConverter convertCToF]]; STAssertEquals(100.0f, [newTemperatureInF floatValue], @"Expecting 100.0; we got %f", [newTemperatureInF floatValue]); }

Abu-Mostafa, Y S (1989) Learning from hints in neural networks Journal of Complexity, 6(2), 192-198 Bergadano, F, & Giordana, A (1990) Guiding induction with domain theories In R Michalski et al (Eds), Machine learning: An art$cial intelligence approach 3 (pp 474-492) San Mateo, CA: Morgan Kaufmann Bradshaw, G, Fozzard, R, & Cice, L (1989) A connectionist expert system that really works In Advances in neural information processing San Mateo, CA: Morgan Kaufmam Caruana, R (1996) Algorithms and applications for multitask learning Proceedings of the 13th International Conference on Machine Learning San Francisco: Morgan Kaufmann Cooper, G C, & Herskovits, E (1992) A Bayesian method for the induction of probabilistic networks from data Machine Learning, 9, 309-347 Craven, M W (1996) Extracting comprehensible modelsfrom trained neural networks (PhD thesis) (UW Technical Report CS-TR-96-1326) Department of Computer Sciences, University of Wisconsin-Madison

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