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The estimation error depends on the choice of F (x, W ) or F (x, W ), that is, the functions . Note that the standardized F improves the stability and convergence, but the asymptotic error for the batch mode and also on-line learning depends on . In order to improve the error, an adaptive choice of is useful. An adaptive choice of is also useful for guaranteeing stability. When is derived from the true probability distributions of the sources, the estimated W is MLE, and is e cient in the sense that the asymptotic error is minimal and is equal to the inverse of the Fisher information matrix. However, it is highly expensive to estimate the probability density functions of the sources. Instead, we use a parametric family of , a = a (y; a ) for each source sa and update the parameter a which speci es a by a = . a (10.73) (10.72)

TransferFunds Command (copy)

We expand the above equation substituting Equations3.18,3.4,3.1 and 3.19 into it, yieldby ing the MSE expression : of

The help command is a descriptive command that explains the help that is available while navigating the command structure. Its output is as follows:

One way to think about a table is as a container for information. The container consists of horizontal rows and vertical columns. If someone speaks of a ve-by-four (5 4) table, by convention and agreement this refers to a table that is ve columns wide and four rows high.

As well as using preset contours, you can also create your own contour shapes. To do this, choose an existing contour to work on and then click the contour graph in the Contour box. The Contour Editor dialog box, shown in Figure 10.15, appears, allowing you to edit the contour and, optionally, save it as a new preset. By default, you re editing the contour preset you clicked on, but you can change this by choosing a different preset from the Preset menu at the top of the dialog box. If you start editing a contour, the value in the Preset menu changes to Custom.

(7.50) (7.51)

target BER SNR(BPSK)[dB] 2.16

Periodic Streams , RFC 3432, November 2002, IETF. [RKT + 94] R. Ramjee, J. Kurose, D. Towsley and H. Schulzrinne, Adaptive playout mechanisms for packetized audio applications in wide-area networks , in Proc. INFOCOM 94 , 1994, p. 680 ff. V. R is nen and A. Lakaniemi, A General a a Method for Analyzing and Generating Packet Loss patterns , in Proc. ICC 02 , New York, IEEE, 2002. V. R is nen and J. Rosti, Application-level IP a a measurements for multimedia , in Proc. IWQoS 00 , p. 170 ff, Pittsburgh, IEEE, June 2000. V. Sander, W.A. Adamson, L. Foster and A. Roy, End-to-end provision of policy information for network QoS , in Proc. High Performance Distributed Computing , 2001, p. 115 ff. R.M. Salles, Utility-based scheduling disciplines for adaptive applications over the Internet , IEEE Communications Letters 6, 2002, p. 217 ff. A. Sridharan, S. Bhattacharyya, C. Diot, R. Guerin, J. Jetcheva and N. Taft, On The Impact of Aggregation on the Performance of Traf c Aware routing, Technical Report, University of Pennsylvania, June 2000 (shortened version presented at ITC 17, Salvador da Bahia, Brazil, 24 28, September 2001). O. Schelen, Quality of Service Agents in the Internet , Lule University of Technology, a Department of Computer Science and Electrical Engineering, Sweden, August 1998. Bruce Schneier, Secrets and Lies, New York, John Wiley & Sons, Inc., 2000.