Pruning rule

We therefore formalize Hoyle s prediction as an example, first in the minimal form of a pruning rule, and then extended to the conservative and radical cosmological forms. We identify the source of predictive specificity in anthropic argument and separate from that the source of interpretation of the priors in different applications. 

The constructive model P(A(M) SC C )xN) acts as before, as a filter on the prior P(S( -C)x I A) output from the cosmological theory. In this respect, the anthropic bias is indistinguishable from the pruning rule generated by Hoyle but as it is driven by incomplete theoretical specification rather than mere intractability, the posterior distribution is not provisional and cannot act as a constraint on reductionist refinements of N or X It is not a substitute for a theory of particular initial conditions, because it adds nothing axiomatic to the structure of the causal theories represented by... 

The posterior evidence variable A(M) has three distinguishable roles in the three anthropic interpretations we have presented. It is chosen for predictive specificity in the generation of a pruning rule for appropriateness as a constraint on the observable ensemble in multiverse cosmology arrd for selectivity of those aspects of initial conditions we regard as relevant to modification of beliefs in the Bayesian cosmological interpretation. 

Development of such pruning rules would require empirical work recording and analysing skilled tutors explanations to students. This would also have to examine the extent to which explanations interweave different kinds and styles of explanation e.g. logical, procedural and problem-domain. 

If the number of unrestricted groups is equal to or greater than the number of type K groups, then no restriction is placed on the remaining groups. Otherwise, the pruning rules that accompany the classification are ... 

With the pruning rules limiting the compound search space, feasible compounds can be formed, and their property values are easily computed from group contributions. A final screening and selection step is largely driven by external heuristics. 

The process of extracting rules from a trained network can be made much easier if the complexity of the network has first been reduced. Furthermore, it is expected that fewer connections will result in more concise rules. Setiono (1997a) described an algorithm for extracting rules from a pruned network. The network was a standard three-layer feedforward back-propagation network trained with a pre-specified accuracy rate. The pruning process attempted to eliminate as many connections as possible while maintaining the accuracy rate. 

Setiono, R. (1997a). Extracting rules from neural networks by pruning and hidden-unit splitting. Neural Comput 9,205-25. 

Finally, all these pruned subtrees will be subject to CV, in order to select the optimal tree size. The optimal tree (Fig. 13.11b) is selected as the simplest among those that have a CV error within one standard error deviation of the minimal CV error (26, 28). Another approach to determine the optimal tree size, preferred when a large number of training samples is available, is the use of an independent test set (26). After obtaining the final model, new samples can be classified by using the rules (split criteria) given by the model. 

Peukert et al. (2010a) propose the use of filter operators within match work-flows to prune dissimilar element pairs (whose similarity is below some minimal threshold) from intermediate match results. The threshold is either statically predetermined or dynamically derived from the similarity threshold used in the match workflow to finally select match correspondences. Peukert et al. (2010a) also propose a rule-based approach to rewrite match workflows for improved efficiency, particularly to place filter operators within sequences of matchers. 

Errors in pruning also cause significant problems. Omitted pruned paths generally resulted from our not using reaction rule constraints or nonselective and/or non-intelligent use of the rules. This is one reason why none of SYNLMA s paths represent published syntheses of Ibuprofen (15) in spite of the fact that the requisite rules were in the data base. On the positive side, the synthetic paths to Ibuprofen discovered by SYNLMA are straightforward and would probably work as shown. 

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