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tions; 02.83, call hold and waiting; 02.84, multi-party; 02.85, closed user group; 02.86, advice of charge. All of these had been planned for the rst phase and are similar to xed line (ISDN) supplementary services. Indeed, many of the supplementary services may be applicable across xed and mobile networks. For example: a call forwarding may be to a xed or mobile line; an incoming call, whose number is to be displayed, may be from a xed or mobile network; a closed user group may contain both xed and mobile numbers. Thus the xed and mobile version must be compatible, although not identical. Interestingly, the most attractive service in the market place turned out to be 02.81. It may be because CLI is generally offered free on mobile services, although often charged for in xed networks. In reality, customers like to know who is calling before they answer the call and this has proved most popular. The service was originally modelled on the xed network service, but we tried to simplify it with the restriction part of the service. Unfortunately, just afterwards, the European Commission published a Directive on Privacy which required us to reinstate the parts we had left out, and this delayed the start of that service by over 1 year. We all agreed that full privacy should be provided, although in reality it is rarely used! Some services were developed with some differences from the ISDN speci cation, either due to technical reasons of mobility, or for commercial reasons. The multi-party service was originally conceived to be identical to the ISDN conference call. However, this is a service controlled by an operator where the subscriber pre-books his conference, with a known number of participants, up to 12. The multi-party service works by the subscriber adding



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FIGURE 12.9 Geometric primitives tted to shapes. min-R: minimum area rectangle; max-R: maximum area rectangle; robust-R: best t rectangle equation (12.7); circ-C: circumscribing circle; insc-C: inscribed circle; voss-C, voss-E, voss-R, voss-T: circle, ellipse, rectangle, and triangle tted by Voss and S e s moment-based method [63]. These primitives are used to u generate some of the shape measures described in this chapter.

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[Good 1965] IJ Good The Estimation of Probabilities: An Essay on Modern Bayesian Methods MIT Press, Cambridge, MA, USA 1965 [Goodman et al 1991] IR Goodman, MM Gupta, HT Nguyen, and GS Rogers, eds Conditional Logic in Expert Systems North-Holland, Amsterdam, Netherlands 1991 [Gould 1981] SJ Gould The Mismeasure of Man WW Norton, New York, NY, USA 1981 Reprinted by Penguin Books, New York, NY, USA 1992 [Greiner 1989] W Greiner Mechanik, Teil 1 (Series: Theoretische Physik) Verlag Harri Deutsch, Thun/Frankfurt am Main, Germany 1989 English edition: Classical Mechanics Springer-Verlag, Berlin, Germany 2002 [Greiner et al 1987] W Greiner, L Neise, and H St cker Thermodynamik o und Statistische Mechanik (Series: Theoretische Physik) Verlag Harri Deutsch, Thun/Frankfurt am Main, Germany 1987 English edition: Thermodynamics and Statistical Physics Springer-Verlag, Berlin, Germany 2000 [Grossman and Domingos 2004] Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood.

Constructing a planar topology is an important ingredient of planar graph routing schemes [4,20,22]. In this context, the notion planar refers to a two-dimensional geometric graph with no intersecting edges. The general idea of this routing scheme can be described as follows. A planar graph partitions the plane into faces that are made up of the polygons described by the graph edges. Beginning with the face containing the starting node, planar graph routing accomplishes message forwarding by following a sequence of faces that provide general progress toward the nal destination node. Exploration of a single face and deciding the right sequence of faces can be done in a pure localized manner. Each forwarding node needs information about its immediate neighbors only. A wireless network consisting of nodes that are deployed on a plane de nes a geometric graph in a natural way. In general, the resulting graph is not planar

Proc 21st Int Conf on Machine Learning (ICML 2004, Banff, Alberta, Canada), 46 ACM Press, New York, NY, USA 2004 [Hammersley and Clifford 1971] JM Hammersley and PE Clifford Markov Fields on Finite Graphs and Lattices Unpublished manuscript, 1971 Cited in: [Isham 1981] [Hartley 1928] RVL Hartley Transmission of Information The Bell System Technical Journal 7:535 563 Bell Laboratories, Murray Hill, NJ, USA 1928 [Haykin 2008] S Haykin Neural Networks and Learning Machines (3rd ed) Prentice-Hall, Upper Saddle River, NJ, USA 2008 [Heckerman 1991] D Heckerman Probabilistic Similarity Networks MIT Press, Cambridge, MA, USA 1991 [Heckerman 1998] D Heckerman A Tutorial on Learning with Bayesian Networks In: [Jordan 1998], 301 354 [Heckerman et al 1994] D Heckerman, JS Breese, and K Rommelse Troubleshooting Under Uncertainty Proc 5th Int Workshop on Principles of Diagnosis, 121 130.

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