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frontends for ANNs have been proposed These hybrid systems essentially automate the tedious work of preprocessing, including organizing data available from cases in a manner required by a speci c ANN model An expert system can also be used to select the best ANN paradigm for the data or to determine the optimal sample size of training and test sets Frameworks have been developed for expert system and hybrid technologies to assist in the improvement of ANN training Among the experimental questions posed were those targeting the need for guidelines in the selection of the training data Examining the example of character recognition, we see that an expert system is a natural t for training Genetic algorithms (GAs) are another area of arti cial intelligence (AI) application that can add value to the training and data selection tasks Developments in hybrid systems of this type show that most of the work performed in this area focuses on the advantages of employing GAs to search large spaces and nd an initial set of parameters for training an ANN A number of GA techniques can be used to select optimal values for data and parameters used in the ANN training process Bayesian approaches have recently been used to select the best ANN architecture in a set of several candidate architectures In particular, the Bayesian nonlinear regression model comparison procedure has been utilized to select an ANN architecture under which the training data set achieves the highest level of internal consistency Most of the techniques proposed in the literature attempt to increase the generalizability of ANN by removing inconsistent data or by adding random noise in the data A few researchers illustrate different procedures used to identify the inputs that may signi cantly (in a statistical sense) be related to the output variable While signi cant, none of the approaches focus on preserving the monotonicity property of the orecasting function learned by an ANN In our case, we propose DEA-based prescreening of ANN data to preserve the monotonicity property Our prescreening approach, while preserving monotonicity, doesn t hurt nonlinearity of the training data We view DEA as a complement to other approaches to training data selection When it is desired to increase generalizability of an ANN, DEA may not be an appropriate technique to use However, when it is desired to preserve the monotonicity property, DEA may be a suitable approach for prescreening training data DEA models provide an ef cient frontier, which is consistent with the monotonicity assumption In fact, all the ef cient DMUs (also called the reference set) lie on the ef cient frontier, which satis es the monotonicity property Figure 34 illustrates several frontiers for a single-input/single-output combination Frontier #1 is the ef cient frontier It can be shown that if DMUs lying on frontier #1 are taken out of the analysis, then frontier #2 becomes the next ef cient frontier and so on Assuming that all nine DMUs are used, it can be argued that DMUs lying on the ef cient frontier #1 have an ef ciency score equal to one and DMUs lying on frontier #2 have an ef ciency score of less than one, but the score is higher than the DMUs lying on frontier #3 ANNs use a least-squares error minimizing approach to learn nonlinear forecasting models If only DMUs lying on frontier #1 are used for training, then it can be argued.



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TRAFFIC MODELS AND TRANSIENT LOSS MEASURES in Visual Studio NET Draw ANSI/AIM ode 39 in Visual Studio NET TRAFFIC MODELS AND TRANSIENT LOSS MEASURESNET Control to generate, create, read, scan barcode image in NET applications Finally, for a multiple-state arrival process, we may further limit the analysis to an arbitrary single state of the arrival process and compare transient performance measures computed nder different modeling assumptions With this approach, we have gained some insights into QoS impact of long-range dependence in network traf c The rest of the chapter is organized as follows In Section 132, we rst introduce a framework for traf c modeling that captures the essential property of long-range dependence Within this framework, traf c is modeled by multistate, uid-type stochastic processes When such a process is in a given state, the underlying traf c source generates traf c at a constant rate.

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The time spent by the process in a state is a random variable For the purpose of this chapter, we let the distribution of the random variable be arbitrary As a result, we can construct Markov and LRD traf c models as we wish Then we de ne loss performance measures in the transient state In Section 133, we compare transient loss performance between the traditional Markov models and the LRD models To keep the comparison reasonable, for the Markov and LRD models, except for the distributions of the times spent by the traf c processes in their respective states, we let all other traf c parameters be the same By doing so, the difference in loss behavior between Markov and LRD traf c is only due to the modeling assumption on the underlying traf c process.

We then compare transient loss of Markov and LRD traf c for two cases In the rst case, we assume that both traf c processes are in the same state with the same initial condition characterized by the amount of traf c left in the system when the processes enter the state In the second case, we consider two-state Markov and LRD uids To examine whether it is appropriate to predict loss performance computed according to Markov models in steady state for LRD traf c, in Section 134, we show how to compute steady-state limits of transient loss measures for general two-state uids, and compare transient loss against loss in steady state In Section 135, we discuss the impact of long-range dependence in network traf c, based on the analytical and numerical results obtained We conclude this chapter in Section 13.

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For example, let s say you choose Drawing from the Standard toolbar in Microsoft Word and draw a few hapes. If you select one shape and then select a second shape, selecting the second shape deselects the first shape. However, if you hold down Shift, you can select any number of shapes at a time. AutoCAD lets you select one object at a time if you wish, although it is not the default, probably because selecting more than one object at a time is so common. If you check this option, after selecting one object, you must hold down Shift to select any additional object. If you check this option, after selecting one object you must hold down Shift to select any additional object. Bar Code barcode library on java generate, create bar .Related: .NET QR Code Generator Data, Print QR Code Excel Size, Print QR Code .NET Size

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As your Web Property grows and the number of assets gets larger, the ability to search for assets with specific characteristics becomes even more compelling However, we must be careful with how we implement the Searches property because searching may require a great deal of system resources Performance can certainly ecome a concern if searches are too wide or are used too often The following Searches methods must iterate the entire CMS Content Repository to retrieve the objects requested and should be used sparingly:. ImportantChannels ISBN: 0-321-19444-6. Draw Code 128B In Java Using Barcode drawer for .Related: Generate ITF-14 .NET WinForms , Print EAN-8 Excel , Print QR Code .NET WinForms

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