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a customer with a given number of day minutes is estimated as (x) = with the estimated logit g(x) = 3.92929 + 0.0112717(day mins) For a customer with 100 day minutes, we can estimate his or her probability of churning: g(x) = 3.92929 + 0.0112717(100) = 2.80212 and
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Unimodular polynomial matrix: The (m x m) polynomial matrix P( x) is said to be unimodular or invertible if det P(x) constant =/; 0, that is, det P( x) is a constant function. (See Section 11.3.) Unipotent matrix: An (m x m) matrix A is said to be unipotent or involutory if ,12 1m. For example, for an (n x n) matrix B,
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Hotels have developed various room rate categories to attract different markets. These rates will depend on seasons, number of potential sales in a market, and other factors.
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In most browsers today, you don t actually need to type the protocol information (http://). If you simply type, you normally get the right page on your screen. Why First, the browser assumes you are using the http protocol. Second, index.html (along with default.html or home.html) is one of the file names a server will serve if you don t tell it which page in a directory you want. Servers can be configured to serve any page as the default page; they frequently have a list and go down the list looking for the page defined as the default.
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Online learning of eTS fuzzy models, which is based on Takagi-Sugeno rules, considers their online identi cation to be subject to gradually evolving rules. The procedure in the present work includes recursive subtractive fuzzy clustering, for determination of the antecedent parameters, and recursive least-squares estimation, for determination of the consequent parameters. Angelov and Filev [4] developed an interesting technique for recursive clustering and rule creation that is the starting and fulcral point for the present proposal. The procedure can be summarized as follows: Step 1: Initialization of the rule base. The rule base is initially empty. Rules are created around the centers of the input output clusters, in the subtractive clustering framework. Let xk and yk be the input and output vectors at instant k, respectively. When the rst input output data is coming, z1 x1 ; y1 , it is the single existing cluster in the input output space and at the same time its own center z*. P1(z1 ) is the energy potential of the 1 cluster center z* to be used for subtractive clustering. The cluster center z* is a 1 1 multidimensional point (the number of dimensions is equal to the number of inputs plus the number of outputs) and each input is an antecedent of each fuzzy rule. To obtain from it a fuzzy set for each antecedent, one needs to project the cluster center into each input dimension, and this projection gives the center of the fuzzy set of each antecedent. So the rule base is initialized with the rst data sample, as in (6.1): k 1; R 1; x* xk ; P1 z* 1 1 1 6:1 where k is the actual sampling point, R is the actual number of rules, z* is the rst 1 cluster center; x* is the focal point of the antecedent of rst rule, obtained by 1 projecting z* on the input dimensions. 1 In a mixed-batch real-time learning approach, the rule base can be initialized by existing expert knowledge or based on previous of ine identi cation, de ning
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You ll notice that we re providing details of the commentator inside of Friend of a Friend (FOAF) elements. FOAF is a Resource Description Framework (RDF) vocabulary that gives us a lot of scope for describing people. For more on that, visit the project s home page at
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Looking Ahead
Figure 7.17 Directed web graph of the relationship between the response and several ag variables.
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