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of Moore s Law and its impact on our rapid transformation from an industrial society into an information society. As part of the story, we consider the inventors Thomas Edison, John Fleming, Lee de Forest, John Mauchly, J. Presper Eckert, John Bardeen, Walter Brattain, William Shockley, Robert Noyce, Jack Kilby, and Gordon Moore as they helped to connect circuits and opened the oodgates to information processing. We follow their contributions to the tributary inventions the vacuum tube, transistor, and microchip that feed into the development of the computer creating Moore s Law and the global river of data on the Information Superhighway.

2 Council decision of 25 July 1985 on a de nition phase for a Community action in the eld of telecommunications technologies. 3 Council decision of 14 December 1987 on a Community programme in the eld of telecommunications technologies R&D in advanced communications technologies in Europe (RACE programme).

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a possibly fraudulent costumer can be fixed later, depending, for instance, on an analysis of expected loss based on test data A naive Bayes classifier can also exploit at least part of the information conveyed by any of the attributes, since the attributes are treated independently and thus using one attribute does not restrict using any other However, the main drawback of a naive Bayes classifier, namely the strong independence assumptions, leads to suboptimal performance in this application Therefore it is advisable not to rely on a pure naive Bayes classifier, but to augment it with edges between some of the descriptive attributes If appropriate heuristics are used to select these edges, it is possible to retain the efficiency with which a naive Bayes classifier can be constructed, thus enabling this approach to handle large volumes of data.

The APRI (Advanced Pattern Recognition and Identification) system developed at AT&T [Ezawa and Norton 1995, Ezawa et al 1996] was designed according to the general scheme outlined above It builds a Bayesian network classifier starting from a naive Bayes structure and employs heuristics based on mutual information (cf Sections 724 and 732) to select the attributes used in the classifier and the additional edges between them The Bayesian network construction in APRI has four phases In the first phase the database is scanned to determine the domains of all attributes This phase may involve a discretization of continuous attributes using an information-based approach In the second phase the descriptive attributes to be used in the classifier are selected To this end, the mutual information of the class attribute C and each descriptive attribute A is computed.

Then a minimal set of attributes M U (where U is the set of all descriptive attributes) is determined, which satisfies Imutual (C, A).

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Using dig, you can also query for a specific record type:

cross is also useful in case a polar becomes misaligned in its holder, requiring the operator to readjust the polar s orientation. Using the telescope eyepiece or Bertrand lens, partially close the condenser aperture diaphragm so that bright outer regions of depolarized light visible at the edge of the aperture field are blocked. This greatly improves the extinction factor of the microscope optics. Switching back to viewing mode, the field of view at extinction should now be very dark. Normally it should only be necessary to rotate the specimen slide to examine the object at different azimuthal angles, leaving the positions of the polarizer and analyzer fixed and unchanged. Because it is often necessary to rotate specimens during examination, it is convenient to use a specially designed rotating stage that is marked in degrees at its periphery. Rotating stages must be adjusted so that their center of rotation is superimposed on the optic axis of the microscope.

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