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EBNN generalizes more accurately than standard BACKPROPAGATION, especially when training data is scarce For example, after 30 training examples, EBNN achieved a root-mean-squared error of 55 on a separate set of test data, compared to an error of 120 for BACKPROPAGATION Mitchell and Thrun (1993a) describe applying EBNN to learning to control a simulated mobile robot, in which the domain theory consists of neural networks that predict the effects of various robot actions on the world state Again, EBNN using an approximate, previously learned domain theory, outperformed BACKPROPAGATIONBACKPROPAGATION Here required approximately 90 training episodes to reach the level of performance achieved by EBNN after 25 training episodes O'Sullivan et al (1997) and Thrun (1996) describe several other applications of EBNN to real-world robot perception and control tasks, in which the domain theory consists of networks that predict the effect of actions for an indoor mobile robot using sonar, vision, and laser range sensors EBNN bears an interesting relation to other explanation-based learning methods, such as PROLOG-EBG described in 11 Recall from that chapter that PROLOG-EBG constructs explanations (predictions of example target values) also based on a domain theory In PROLOG-EBG explanation is constructed from a the domain theory consisting of Horn clauses, and the target hypothesis is refined by calculating the weakest conditions under which this explanation holds Relevant dependencies in the explanation are thus captured in the learned Horn clause hypothesis EBNN constructs an analogous explanation, but it is based on a domain theory consisting of neural networks rather than Horn clauses As in PROLOG-EBG, relevant dependencies are then extracted from the explanation and used to refine the target hypothesis In the case of EBNN, these dependencies take the form of derivatives because derivatives are the natural way to represent dependencies in continuous functions such as neural networks In contrast, the natural way to represent dependencies in symbolic explanations or logical proofs is to describe the set of examples to which the proof applies There are several differences in capabilities between EBNN and the symbolic explanation-based methods of 11 The main difference is that EBNN accommodates imperfect domain theories, whereas PROLOG-EBG does not This difference follows from the fact that EBNN is built on the inductive mechanism of fitting the observed training values and uses the domain theory only as an additional constraint on the learned hypothesis A second important difference follows from the fact that PROLOG-EBG learns a growing set of Horn clauses, whereas EBNN learns a fixed-size neural network As discussed in 11, one difficulty in learning sets of Horn clauses is that the cost of classifying a new instance grows as learning proceeds and new Horn clauses are added This problem is avoided in EBNN because the fixed-size target network requires constant time to classify new instances However, the fixed-size neural network suffers the corresponding disadvantage that it may be unable to represent sufficiently complex functions, whereas a growing set of Horn clauses can represent increasingly complex functions Mitchell and Thrun (1993b) provide a more detailed discussion of the relationship between EBNN and symbolic explanation-based learning methods. rdlc pdf 417 PDF417 Barcode Creating Library for RDLC Reports | Generate ...
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