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In response to the call for a more systematic and standardised approach, guidelines for clinical practice and its delivery are being developed internationally. The American Psychiatric Association was the rst to publish guidelines for the treatment of eating disorders (APA, 1992) and published a revised edition in 1993 and 2000. Guidelines currently in circulation in the UK are those published from a collaboration between the Royal College of Psychiatrists (2001) and the Eating Disorders Association (1995) and also the British Psychological Society (Bell et al., 2001). The National Institute of Clinical Excellence (NICE) is also planning the development of eating disorder clinical guidelines (www.nice.org.uk). Australian guidelines will shortly appear and the National Commission in Italy are also developing guidelines emphasising evidence-based practice (Ruggiero et al., 2001). Regarding a broader classi cation of eating disorders, obesity is also a public health concern frequently revisited by the World Health Organisation. In the UK the most comprehensive guidelines for the management of obesity are found in the SIGN report (Scottish Intercollegiate Guidelines Network, 1996) and these are due to be updated.

To find approximate decompositions we need, as in the relational case, a measure for the strength of dependences. For a strictly positive probability distribution, such a measure can easily be derived from the Kullback Leibler information divergence by comparing two specific distributions, namely the joint distribution over a set of attributes and the distribution that can be computed from its marginal distributions under the assumption that the attributes are independent. For two attributes this measure can be defined as follows [Kullback and Leibler 1951, Chow and Liu 1968]: Definition 7.1.4 Let A and B be two attributes and P a strictly positive probability measure. Then Imut (A, B) =

Once detailed design is underway it is necessary to consider very carefully the differences between centralized and distributed systems and the implications of factors including the life cycle, object references, request latency, securityand so on We discuss what differentiates distributed object design from local object design in the last section..

(2.6)

The uplink DPCH uses softer handover during intra Node B HS-DSCH to HS-DSCH handover Under such conditions the uplink HS-DPCCH may also be regarded as being in a two-way softer handover, so Rake ngers for demodulation of the HS-DPCCH are allocated to both cells in the user s active set This implies that uplink coverage of the HS-DPCCH is improved for users in softer handover 61144 HS-DSCH to DCH handover Handover from an HS-DSCH to a DCH may potentially be needed for HSDPA users that are moving from a cell with HSDPA to a cell without HSDPA (as illustrated in.

FIGURE 1.1 Last-mile bottleneck between a user s desktop or laptop computer and the metro and long-haul facilities.

571 571 572 572 574 574

(7.21)

for i = 0, . . . , 7. (9.20)

e where wik is the weighted distance of the training pattern ^i from class Ck , and the positive constants fd and fe are the denominational and exponential fuzzy generators controlling the amount of fuzziness in the class membership set. Fuzziness Measure Fuzziness is incorporated at the node level by changing the decision function from classical entropy to a fuzzy measure, FM. This is de ned as [41] " # l N X 1X nk nk min mik ; 1 mik log2 FM I N N N i 1 k 1 " # l N X 1X min mik ; 1 mik pk log2 pk 3:36 N i 1 k 1 where N is the number of pattern points in the training set, l the number of classes, ni the population in class Ci , pk the a priori probability of the kth class, and mik the membership of the ith pattern to the kth class. The expression for FM is de ned so that when the class membership values are zero or 1 (crisp), it represents the classical entropy. The rst term of equation (3.36) ensures that pattern points lying in overlapping regions are assigned lower weights during construction of the decision tree, which is an intuitively appealing idea. The reason for this lower weighting is that such ambiguous patterns (i.e., having m values close to 0.5) lead to an increase in FM, thereby creating an obstacle to its minimization. Estimating Confidence of the Nodes Mandal et al. [43] provided a scheme to calculate the con dence factor (CF) for a rule base to infer how much a point belongs

5:3:3

As you debug your application and find errors, you will see these errors in your JSP pages or Java classes. As you make changes and test them, you need to be sure that the changes are reflected in the server. By default, your server may not recompile JSP pages or reload classes that have been changed without being restarted. If you think you've fixed an error but your changes don't appear to be reflected, try restarting the server. Also, examine your server documentation to see how you can set it up to reload classes that have been changed and recompile JSP pages that have been altered.

The tolerance sensitivity S is de ned as the ratio of variance of peak eld strength 2 produced by errors of variance T (Uzsoky and Solymar, 1956): 2 J J* j ( E )2 2 J A J* J J* 2 S = E = E2 = = 2 j 2 J A J* j j