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From this algorithm it is clear that an initial training phase has to take place. At rst glance, this seems to be a drawback versus other approaches that are able to perform incremental learning from the start. However, it should be emphasized that in industrial applications usually an of ine modeling step is required in order to get a feeling of the performance of the models and to be able to carry out an analysis of some model components (for examining reliability and reasonability). Furthermore, an initial model building (which is also possible during online mode when carried out on just the rst few dozen samples) delivers a good rst guess of an appropriate setting of the most important parameter (the vigilance r, which guides the number of rules) in FLEXFIS, especially when applied within a best parameter grid search procedure connected with a crossvalidation process (see also section 5.5).

This result is similar to the result derived in 5 for the competitive optimality of Shannon codes. By considering examples, it can be seen that it is not possible to get a better bound on the probability that Sn > Sn . Consider a stock market with two stocks and two possible outcomes, 1 with probability 1 , 1, (X1 , X2 ) = (16.78) 1 (1, 0) with probability . In this market the log-optimal portfolio invests all the wealth in the rst stock. [It is easy to verify that b = (1, 0) satis es the Kuhn Tucker conditions.] However, an investor who puts all his wealth in the second stock earns more money with probability 1 . Hence, it is not true that with high probability the log-optimal investor will do better than any other investor. The problem with trying to prove that the log-optimal investor does best with a probability of at least 1 is that there exist examples like the 2 one above, where it is possible to beat the log-optimal investor by a small amount most of the time. We can get around this by allowing each investor an additional fair randomization, which has the effect of reducing the effect of small differences in the wealth.

Krasne, F B (1969) Excitation and habituation of the cray sh escape re ex: The depolarization response in lateral giant bers of the isolated abdomen Journal of Experimental Biology, 50, 29 46 Krech, D, Rosenzweig, M R, & Bennett, E L (1960) Effects of environmental complexity and training on brain chemistry Journal of Comparative Physiology and Psychology, 53, 509 519 Lashley, K S (1929) Brain mechanisms and intelligence Chicago: University of Chicago Press Lashley, K S (1938) Experimental analysis of instinctive behavior Psychological Review, 45, 445 471 LeDoux, J E (2000) Emotion circuits in the brain Annual Review of Neuroscience, 23, 155 184 Levine, S (1971) Stress and behavior Scienti c American, 224, 26 31 Lindsley, D B (1936) Brain potentials in children and adults Science, 83, 254 Lindsley, D B (1951) Emotion In S S Stevens (Ed), Handbook of experimental psychology (pp 473 516).

Notice that Figure 11-4 contains a 7805 5 VDC voltage regulator. Underneath the SitePlayer module is a MAX232 transceiver chip, which is also familiar. You could build an exact duplicate of this circuit if you wanted (in fact NetMedia provides the schematic for it). The only slightly oddball part is the LF1S022, which is a 10base-T filter and RJ-45 jack.

New York: Wiley Ling, G, & Gerard, R W (1949) The normal membrane potential of frog sartorius bers Journal of Cellular and Comparative Physiology, 34, 383 396 Luria, A R (1981) Language and cognition New York: Wiley MacLean, P D (1949) Psychosomatic disease and the visceral brain : Recent developments bearing on the Papez theory of emotion Psychosomatic Medicine, 11, 338 353 Marshall, W H, Woolsey, C N, & Bard, P (1941) Observations on cortical sensory mechanisms of cat and monkey Journal of Neurophysiology, 4, 1 24 Maturana, H R, Lettvin, J Y, McCulloch, W S, & Pitts, W H (1960) Anatomy and physiology of vision in the frog (Rana pipiens) Journal of General Physiology, 43, 129 176 McGaugh, J L (1989) Involvement of hormonal and neuromodulatory systems in the regulation of memory storage Annual Review of Neuroscience, 12, 255 288 McGaugh, J L.