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dif cult to measure, for any of the features that go to make up the various systems on offer. Thus, it will be dif cult to estimate the reliability of speaker veri cation schemes, and so it is unlikely that they will be trusted on their own, for critical applications. Nevertheless, in communications, speech is one of the easiest data sets to collect and there are indications that speaker veri cation does give a modest degree of protection. We could say, for example, that from a population of a 1,000 voices, we could reasonably claim that we could narrow the possibility down to 50 or even less, with a good chance of being correct. If, for example, the authentication procedure comprised asking someone to speak their personal identity number (PIN), voice recognition would thereby add signi cantly to the security of the process over that of simply keying in the PIN. Regarding the details of voice recognition, there are a large number of parameters that have been tried: voice pitch, frequency spectrum, vocal tract resonances (formants) and derivations thereof. Some are more resistant than others to the noise and distortion problems of communication networks, or require less computational activity.

Thompson detected the electron (1897), proving the ancient Greek theory that atoms were at the foundation of matter; Max Planck proposed the quantum theory of light (1900); Gregor Mendel s discovery of how inherited traits are passed was nally accepted (1900); Walter Reed conquered yellow fever (1902); and Wilbur and Orville Wright changed human transportation forever at Kitty Hawk (1903)..

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Marketing research has a vital role to play in ensuring that new products launched onto the market are successful ventures, rather than dismal failures. The main input that marketing research makes into the new product development process is in the areas of:

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Note also that both families of evaluation measures may be criticized on the grounds that, at least formally, the factor can also be interpreted as the assumption that we observed the database ( + 1) times, which would be ridiculous from a strictly statistical point of view However, in our derivation of these families of measures we emphasized that the factor results from a choice of the prior distribution and it has to be admitted that the choice of the prior distribution is, to some degree, arbitrary Of course, there are strong arguments in favor of a uniform prior distribution, for instance, the insufficient reason principle (cf Section 243) However, when learning Bayesian networks from data choosing a uniform prior distribution introduces a tendency to select simpler network structures.

This tendency results from the fact that the size of the space of conditional probabilities is larger for more complex structures and thus, in a way, there are more bad choices of conditional probabilities (that is, choices that make the database unlikely) Consequently, a more complex graph may be judged to be worse than a simpler graph, although there is a choice of probability parameters for which the database is much more likely than with any choice of parameters for the simpler graph (Another explanation, which is based on the close formal relation of the K2 metric and a minimum description length measure, is discussed in the next section) It has to be admitted, though, that such a tendency can be desirable in order to avoid overfitting the data However, even then it is usually convenient to be able to control the strength of this tendency.

It is noted that A Z 1 and B Z 1 are polynomials of the time-shift operator Z 1 and normal arithmetic operations may be carried out under certain conditions. De ning a new function H Z 1 as B Z 1 divided by A Z 1 and expanding the resulting H Z 1 in terms of operator Z 1, we have H Z 1

With the above families of evaluation measures such control can be exerted via the parameter : the greater the value of this parameter, the weaker the tendency to select simpler structures..

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