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52 Radial Basis Function Neural Networks Algorithm 53 Two-Phase RBFNN Training nitialize wkj , k = 1, , K and j = 1, , J; Initialize ji , j = 1, , J and i = 1, , I; Initialize j , j = 1, , J; while LVQ-I has not converged do Apply one epoch of LVQ-I to adjust j , j = 1, , J; Adjust j , j = 1, , J; end t = 0; while gradient descent has not converged do Select an input pattern, (zp , tp ); Compute the weight step sizes,. Paint Data Matrix ECC200 In VB.NET Using Barcode maker for Visual Studio .NET .Related: QR Code Generator .NET WinForms , EAN-13 Generating Java , Print Data Matrix ASP.NET

Java Server Page (JSP) tag libraries, which are originally uilt for use within JSP systems Java Server Pages, which are the JSP equivalent of ColdFusion templates Flash Remoting, which enables ColdFusion to easily communicate with Flash movies Web services, which publish portions of a ColdFusion application s functionality for use by other application servers, including non-ColdFusion servers COM (Component Object Model) objects, which are software objects that can communicate with one another on the Microsoft Windows platform CORBA (Common Object Request Broker Architecture) objects, which can communicate with one another over networks and between platforms The operating system (Windows, Unix, and so on), through batch files and executables Each of these technologies can and do take up entire books by themselves, because they are big topics. ColdFusion, however, makes integrating them with your application a relatively simple matter by using a simple interface usually just a matter of one or two CFML tags. For more information about extending ColdFusion, read chapters 17, 18, and 22 through 29. In addition, refer to Developing Web Applications with ColdFusion MX in your ColdFusion MX documentation set to learn about creating CFX custom tags, which are custom tags written in Java or C++ for use in ColdFusion. You shouldn t just slam through the defaults in installing ColdFusion MX either, because you have important choices to make regarding your ebserver configuration. For example, ColdFusion MX can install its own standalone web server for development purposes during the installation process, or it can use your currently installed web server, so watch what you re doing. Good luck!. tomake ean8 for java. Control barcode pdf417 data .Related: .NET QR Code Generator Data, Data Matrix Generator .NET , PDF417 Generation .NET

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In bankruptcy prediction, and in other classi cation problems, the costs of Type I and Type II errors are important considerations for a decision maker There is evidence of an asymmetric cost structure, with an estimate of Type I error cost that is higher than Type II error cost The cost of Type I error was estimated from the loan loss experience of banks, and the cost of Type II error was the opportunity cost of not lending to a nonbankrupt rm because it was predicted to become bankrupt Past studies employing LDA, ANNs, and GAs do not allow a user to incorporate asymmetric costs of misclassi cation As a result, the Type I and Type II error costs are considered equal in most past studies A probabilistic approach such as logit, however, allows a decision maker to trade off between Type I and Type II error by setting the cutoff probability The technique, however, does not provide any guidelines for deciding on the cutoff probabilities, which makes it dif cult to use Recently, GAs have been used to learn connection weights for an ANN Among the advantages of using GA to train ANN connection weights are exibility of design of the tness function and global search The exibility of the design of the tness function makes it possible to use GAs to learn connection weights of an ANN so that Type I error is minimized Furthermore, the discriminant function learnted by using GA-based ANN is a nonlinear function and is ikely to perform better than linear discriminant functions developed by LDA and GAs The preceding discussion provides the basic underpinnings of the bankruptcy prediction problem, that is, information data facts meaning However, it is not until a comparison of the various linear and nonlinear techniques are made and the evidence that Type I and Type II errors are not equal, that actual knowledge or information in context has been supplied to move the decision maker toward the smart data wisdom paradigm shift The knowledge experience context is to recognize that most classi cation systems for predicting bankruptcy have attempted to minimize the misclassi cations The minimizing misclassi cation approach assumes that Type I and Type II error costs of misclassi cation are equal This long-held assumption may be a fallacy Remember that wisdom applied knowledge knowledge experience The lesson to be learned is that there is evidence that these Type I and Type II costs are not equal and incorporating these costs into the classi cation systems can lead to superior classi cation systems The common thread among government and private enterprise is data In the following case, we demonstrate what we stated on page 45 how the enterprise receives data from external sources and transforms this data for internal purposes that translate into higher yield outputs and helps to prevent accounting fraud in the organization The outputs are sent on to other users in the community some of which are end users while others will employ the data as input in their own processes in order to prevent fraudulent activities in their enterprises The purchase, lease, and use of data falls under constraints and agreements, such as generally accepted accounting standards, whereby there is consideration monetary value exchanged for use either explicitly or implicitly Material misuse of these assets can be extremely costly to the organization It is the job of the enterprise executive to.

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know the data assets and protect them, and these assets are the collateral for which the enterprise receives revenue or bene ts Executives must know the data on which the enterprise performance is dependent They must know about the sources of data and understand how they may in uence the sources of data such that the data received is optimally engineered for their use Likewise, top executives must know where their data outputs are headed and they must be concerned about how well their outputs satisfy the needs of the user community The integration of smart data, application of arti cial intelligence techniques, and adoption of a smart data SOE paradigm can be used to counter accounting fraud ascos, such as Enron and World Com, and are set forth in the case Utilization of Data Mining Techniques to Detect and Predict Accounting Fraud: A Comparison of Neural Networks and Discriminant Analysis [20] Accounting information systems enable the process of internal control and external auditing to provide a rst-line defense in detecting fraud There are few valid indicators at either the individual or the organizational level which are reliable indicators of fraud prevention Recent studies have shown that it is nearly impossible to predict fraud In fact, many of the characteristics associated with white-collar criminals are precisely the traits that organizations look for when hiring employees This case proposes the use of information systems and smart data and utilization of the SOE model to deal with fraud through proactive information collection, data mining, and decision support activities Here is an example of citizens being recipients of services provided by government enterprise All participants in detecting accounting fraud have a symbiotic relationship Success in detecting and deterring accounting fraud is measured in an aggregation that is best for citizens, best for government, best for government trading partners, and best for allies esults show that while traditional methods, such as discriminant analysis, yielded 504% of original grouped cases correctly classi ed, no signi cant relationship was found (0149) between attitude, morale, internal controls, increases in expenditures, and whether or not fraud was actually committed Cronbach s alpha of reliability was 06626 and offered somewhat reliable results in this exploratory research Neural networks did a much better job of predicting fraud (759%) than discriminant analysis (504%) Neural networks were able to nd patterns in the training set and then correctly identify more than three-fourths of similar patterns in the testing set Therefore it can be concluded that neural networks outperform discriminant analysis by 255% in this data set It is not until a comparison of the various linear and nonlinear techniques are made that actual knowledge or information in context has been supplied to move the decision maker toward the smart data wisdom paradigm shift The knowledge experience context is to recognize that the application of arti cial intelligence mechanisms for predicting fraud involves not only the context of a symbiotic relationship between the stakeholders, but also a realization that, in a smart data sense, we do not necessarily live in a parametric, bellshaped world Nonlinear relationships must be taken into account as well if we are to nd true wisdom .

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