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In a very real sense, a neural network is actually a multiple regression, albeit, nonlinear, and the correlation of a neural net s output with the target may be construed as a multiple correlation coefficient The multiple correlation obtained between a net s output and the target may be corrected for shrinkage to obtain some idea of how the net might perform on out-of-sample data Such shrinkage-corrected multiple correlations should routinely be computed as a means of determining whether a network has merely curve-fit the data or has discovered something useful The formula for correcting a multiple correlation for shrinkage is as follows:.



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Oct 31, 2017 · Convert PDF to Pages with PDF Converter Pro for Mac. Load a PDF File. After opening PDF Converter Pro, drag and drop a PDF document into the program to convert it. Select Pages as Output. Click the "W" icon to select "Word Processing(.pages)" as output format to convert.

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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.

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A FORTRAN-style expression was used for reasons of typsetting. In this formula, SQRT represents the square root operator; N is the number of data points or, in the case of neural networks, facts; P is the number of regression coefticients or, in the case of neural networks, connection weights; R represents the uncorrected multiple correlation; and RC is the multiple correlation corrected for shrinkage. Although this formula is strictly applicable only to linear multiple regression (for which it was originally developed), it works well with neural networks and may be used to estimate how much performance was inflated on the in-sample data due to curve-fitting. The formula expresses a relationship between sample size, number of parameters, and deterioration of results. The

expanding the bandwidth it is capable of delivering as well as the distance over which that bandwidth can be delivered.

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To write in-line IronRuby code you need to add the following script tag with the appropriate type and class information, and place the IronRuby code within the tag. <script type="application/ruby" class="Class Name Goes Here"> IronRuby Code Goes Here </script> To write in-line IronPython code, you need to add the following script tag with the appropriate type and class information and place the IronPython code within the tag. <script type="application/python" class="Class Name Goes Here"> IronPython Code Goes Here </script> You can also use text/ruby or text/python script type instead of application/ruby or application/python respectively.

We have now discussed the functions carried out at each layer of the OSI Model Layers six and seven ensure application integrity; layer five ensures security; layer four guarantees the integrity of the transmitted message; layer three ensures network integrity; layer two, data integrity; and layer one, the integrity of the bits themselves Thus transmission is guaranteed on an end-to-end basis through a series of protocols that are interdependent upon each other and that work closely to ensure integrity at every possible level of the transmission hierarchy So let s now go back to our e-mail example and walk through the entire process The Eudora e-mail application running on the PC creates a message at the behest of the human user4 and passes the message to the Application Layer.

statistical correction embodied in the shrinkage formula is used in the chapter on neural network entry models.

The Application Layer converts the message into a format that can be universally understood as an e-mail message, in this case X400 It then adds a header that identifies the X400 format of the message The X400 message with its new header is then passed down to the Presentation Layer, which encodes it as ASCII, encrypts it using PGP, and compresses it using a British Telecom Lempel-Ziv compression algorithm After adding a header that details all this, it passes the message to layer five The Session Layer assigns a logical session number to the message, glues on a packet header identifying the session ID, and passes the steadily growing message down to the Transport Layer Based on network limitations and rule sets, the Transport Layer breaks the message into eleven packets and numbers them appropriately Each packet is given a header with address and quality-of-service information.

Although, for statistical reasons, the system developer should seek the largest sam ple possible, there is a trade-off between sample size and representativeness when dealing with the financial markets. Larger samples mean samples that go farther back in time, which is a problem because the market of years ago may be fundamentally different from the market of today-remember the S&P 500 in 1983 This means that a larger sample may sometimes be a less representative sample, or one that confounds several distinct populations of data! Therefore, keep in mind that, although the goal is to have the largest sample possible, it is equally important to try to make sure the period from which the sample is drawn is still representative of the market being predicted.

The packets now enter the chained layers, where they will first encounter the network The Network Layer examines each packet in turn, and, based on the nature of the underlying network (connection-.

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