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Case density, 21, 108, 190 Case distance, 19. See also Fuzzy similarity; Similarity Case evaluation, 207 Case feature weighting genetic algorithms, 102 gradient-descent technique, 99 learning, 169 neural networks, 101 Case generation. See also Case selection fuzzy representation, 52 linguistic representation, 54 methodology, 56 1-NN classi cation, 62 reducts, 52. See also Rough sets retrieval, 60 rule generation, 58 Case indexing back propagation neural network, 71 Bayesian model, 64 de nition, 23 genetic algorithms, 219 prototype-based neural network, 69 traditional method, 63 Case inference , 215 Case organization breadth- st graph search, 35 discrimination networks, 36 at memory, 35 hierarchical memory, 36 parallel search, 36 redundant discrimination networks, 36 serial search, 35 shared feature networks, 35 Case representation comparison, 42 at feature-value vector, 23 fuzzy frame, 214 fuzzy sets, 80, 201, 213, 224 object-oriented, 40 predicate, 41 relational, 38 Case retrieval fuzzy neural network, 206 fuzzy sets, 215. See also Fuzzy similarity genetic algorithms, 220 neural network, 105, 225 rough-fuzzy approach, 60 Case selection. See also Case generation Bayes classi er, 119 b-index, 127 deletion, 110 entropy, 126





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joint laxity, pain, endurance, recovery rate), the latter of which are thought to be affected by biological and psychological factors The paths between psychological factors and both intermediate biopsychological outcomes and sport injury rehabilitation outcomes are proposed as bidirectional Psychological Models Although the biopsychosocial model (Brewer et al, 2002) provides a broad-based framework for investigating the sport injury rehabilitation process and offers potential explanations for how psychological factors can affect sport injury rehabilitation outcomes, it does not articulate proposed relationships among specific psychological factors Consequently, to better understand the psychology of sport injury rehabilitation, it is instructive to examine psychological models that elaborate on some of the features of the biopsychosocial model Most of the extant psychological models in the sport injury rehabilitation literature can be categorized as either stage models or cognitive appraisal models (Brewer, 1994).





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is not generally equal to P. If one wants to compute the precise change in for a unit increase in X , one generally must evaluate P(x + 1) P(x); see DeMaris (1993) for an extended discussion of this issue. Standardized Coef cients In linear regression, standardized coef cients are used to compare the relative impacts of different predictors in the same equation. The standardized coef cient is the product of the unstandardized coef cient times the ratio of the standard deviation of X k to the standard deviation of Y . In logistic regression, calculating a standardized coef cient is not as straightforward. Recall the latent-variable formulation for the logistic regression model, discussed earlier. The logistic regression coef cient, k , can, of course, be interpreted as the change in Y for each unit increase in X k . If we had an estimate of the standard deviation of Y , we could compute the standardized version of this coef cient as

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COUGAR: this protocol [35] abstracts from the format used to compile a query (which is left to be application-dependent) and utilizes data aggregation to reduce the amount of transmission in the network It differs from DD in that it makes nodes select a leader to perform data aggregation The leader itself is in charge of data forwarding to the sink The base station provides a query that is compliant with the application-speci c format and which incorporates a method to select leaders among the nodes.

The main advantage of this procedure is that the network itself operates in a way that is independent of the application, but the architecture also has some drawbacks: rst, the nodes need query processing functions which may in turn generate extra overheads and energy consumption, and second, the leaders need to be dynamically changed in order not to become bottlenecks or to deplete available energy too fast Routing protocols with random walks: routing based on random walks [36] tries to achieve load balancing through multipath routing in WSNs It assumes that the network is made of a large number of nodes with limited or no mobility which may go to sleep or wake up at random times A regular grid is assumed so that nodes fall exactly on a crossing point, even if the topology is irregular (ie nodes may not cover all of the grid crossing points).

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