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Download e-book for iPad: Computer Aided Decision Support in Telecommunications by K A E Totton, P G Flavin (auth.), P. G. Flavin, K. A. E.

By K A E Totton, P G Flavin (auth.), P. G. Flavin, K. A. E. Totton (eds.)

ISBN-10: 9400900813

ISBN-13: 9789400900813

ISBN-10: 9401065241

ISBN-13: 9789401065245

In bringing jointly this publication, the editors have stored targets in brain. to begin with, the aim of teaching the reader via giving an perception into the wealth of computing and mathematical strategies now getting used to construct choice help structures. Secondly, of aiming to stimulate the mind's eye by means of together with an eclectic mixture of contributions from a variety of company parts to illustrate that there's no box during which glossy choice aid innovations can't usefully be utilized. The quintessence of determination help platforms is they are designed to aid humans in developing the simplest plan of action in a given scenario yet to not automate or inform them prescriptively the best way to in attaining a goal.

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Classes within the data can be expected to appear as distinct clusters in the space. Parametric statistical approaches to classification use knowledge of class distributions to determine planes in the multi-dimensional space that divide classes from each other. Non-parametric statistics (considered in the next section) classify without making assumptions about the underlying distributions of classes (the authors are indebted to Taylor et al [3] for this distinction). 1 Parametric statistical classification Fisher [26] first proposed the idea of a linear discriminant in 1936 to define lines in two-dimensional space (or planes in multidimensional space) that The process of generalization and specialization is more commonly referred to as candidate elimination.

REFERENCES 1. Anderberg M R: 'Cluster analysis for applications', Academic Press (1973). 2. Jain A K A and Dubes R C: 'Algorithms for clustering data', Prentice Hall (1988). 3. Taylor C, Michie D and Spiegelhalter D: 'Machine learning, neural and statistical classification', Ellis Horwood (1994). 4. Totton K A and Limb P R: 'Electronic diagnosis using a multilayer perceptron', BT Technol J, 10, No 3, pp 97-102 (1992). 5. Tattersall G D et al: 'Feature extraction & visualization of decision support data', BT Technol J, 10, No 3, pp 110-123 (1992).

1 Classification using neural networks Neural networks, or more correctly artificial neural networks (ANN), is a term given to a wide selection of algorithms being used increasingly in many areas of data mining. A full description of ANNs is beyond the scope of this chapter but an outline of one particular ANN is given as a starting point for further study by the reader if desired. Research into ANNs was begun by scientists interested in how the brain functioned. A significant contribution was made by McCulloch and Pitts [9] in which they derived theorems related to models of neural systems.

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Computer Aided Decision Support in Telecommunications by K A E Totton, P G Flavin (auth.), P. G. Flavin, K. A. E. Totton (eds.)

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