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How could such a loss have been avoided How could we have at least preserved a portion of the pro ts after that initial 150 percent increase Every chart is different, of course, but in this case, it probably would have been best to monitor the stock for its series of higher highs and higher lows. As long as the stock was ascending with a pattern of higher highs and higher lows, it would be a solid hold. When the price cut beneath one of the prior lows, however, it would have been a good time to get out. In this instance, it would have meant a pro t of about 50 percent (instead of 150 percent), but that s certainly a lot better than the loss that would have ensued if the position had simply been held in spite of its price action. One of the most popular securities on the market is the QQQ, often referred to as the cubes and with the ticker symbol QQQQ. This ETF tracks the performance of the NASDAQ 100 stocks, and the chart in Figure 10.21 shows the QQQ with a well-formed cup with handle and a resistance level of about $40.20. At the time of this chart, the price pushed cleanly past this resistance level, portending good times for this security. Look at the longer-term price perspective in Figure 10.22, however. The price breakout is indicated by the arrow, and as you can see, the price

1986] DA Plaut, S Nowlan, G Hinton, Experiments on Learning by Back Propagation, Technical Report CMU-CS-86-126, Department of Computer Science, Carnegie-Mellon University, 1986 [Plutowski and White 1993] M Plutowski, H White, Selecting Concise Training Sets from Clean Data, IEEE Transactions on Neural Networks, Vol 4, No 2, March 1993, pp 305 318 [Polkowski and Skowron 1998] L Polkowski, A Skowron (eds), Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems, Springer Verlag, 1998 [Potter 1997] MA Potter, The Design and Analysis of a Computational Model of Cooperative Coevolution, PhD Thesis, George Mason University, Fairfax, Virginia, USA, 1997..

Left Lesion (N = 96)

Right Lesion (N = 98)

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NOTE: V P stands for Verbal IQ minus performance IQ. Change refers to the change in Verbal IQ and Performance IQ preoperatively to postoperatively. It equals postoperative IQ minus preoperative IQ; therefore, negative signs refer to lower postoperative IQs. Mean differences between V- and P-IQs, and between

that had not had surgery, they found that the unoperated sample had similar mean IQ improvements. Because the improvements were similar across groups that had surgery and that did not, the observed improvements are most likely attributable to a positive practice effect. Indeed, the most measurable improvements in Chelune et al. s samples were in the P-IQ, which typically has more substantial practice effects than the V-IQ (see 6).

from Smith s (1966c) and Whelan and Walker s (1988) studies of tumors confined to the frontal lobes, and Black s (1976) study of missile wounds that were similarly confined, Table 8.6 reveals very slight V P differences for patients with leftor right-brain lesions. Although the differences are both in the predicted direction, they are too small (2 to 2 1 2 points) to be of consequence.

[Prechelt 1995] L Prechelt, Adaptive Parameter Pruning in Neural Networks, Technical Report TR-95-009, International Computer Science Institute, Berkeley, California, 1995. [Rechenberg 1973] I Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der Biologischen Evolution, Frammann-Holzboog Verlag, Stuttgart, 1973. [Rechenberg 1994] I Rechenberg, Evolution Strategy, in JM Zurada, R Marks II, C Robinson (eds), Computational Intelligence: Imitating Life, IEEE Press, 1994, pp 147 159. [Reed 1993] R Reed, Pruning Algorithms - A Survey, IEEE Transactions on Neural Networks, Vol 4, No 5, 1993, pp 740 747. [Reed et al. 1995] R Reed, RJ Marks II, S Oh, Similarities of Error Regularization, Sigmoid Gain Scaling, Target Smoothing, and Training with Jitter, IEEE Transactions on Neural Networks, Vol 6, 1995, pp 529 538. [Reynolds 1979] RG Reynolds, An Adaptive Computer Model of the Evolution of Agriculture, PhD Thesis, University of Michigan, Michgan, 1979. [Reynolds 1991] RG Reynolds, Version Space Controlled Genetic Algorithms, Proceedings of the Second Annual Conference on Artificial Intelligence Simulation and Planning in High Autonomy Systems, IEEE Press, 1991, pp 6 14. [Reynolds 1994] RG Reynolds, An Introduction to Cultural Algorithms, Proceedings of the Third Annual Conference on Evolutionary Computing, 1994, pp 131 139. [Reynolds 1999] RG Reynolds, Cultural Algorithms: Theory and Application, in D Corne, M Dorigo, F Glover (eds), New Ideas in Optimization, MacGraw-Hill, 1999, pp 367 366. [Ripley 1996] BD Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996. [Robel 1994] A Robel, The Dynamic Pattern Selection Algorithm: Effective Training and Controlled Generalization of Backpropagation Neural Networks, Technical Report, Institut fur Angewandte Informatik, Technische Universitat Berlin, March 1994, pp 497 500. [Rosen and Goodwin 1997] BE Rosen, JM Goodwin, Optimizing Neural Networks Using Very Fast Simulated Annealing, Neural, Parallel & Scientific Computations, Vol 5, No 3, 1997, pp 383 392.

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