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This chapter addressed evolving fuzzy participatory learning, a method based on the participatory learning paradigm to develop evolving fuzzy rule-based models. Participatory learning materializes in the evolving fuzzy participatory method in the form of an

(17.10) in Section 8.3; details apply as given in Seconce the ma-

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Kinsey, A J (1948) Sexual behavior of human male Philadelphia: Saunders Kinsey, A J (1953) Sexual behavior of human female Philadelphia: Saunders Klein, D C (1968) Community dynamics and mental health New York: Wiley Klein, D C (1987) The context and times at Swampscott: My/story American Journal of Community Psychology, 15, 531 538 Korchin, S J (1976) Modern clinical psychology New York: Basic Books Koretz, D S (Ed) (1991) Preventive intervention research centers [Special issue] American Journal of Community Psychology, 19 Leavell, H R, & Clark, E G (1965) Preventive medicine for the doctor in his community, an epidemiologic approach (3rd ed) New York: McGraw-Hill Lemkau, P (1955) Mental hygiene in public health New York: McGraw-Hill Levine, M (1981) The history and politics of community mental health New York: Oxford University Press Levine, M (1998) Prevention and community American Journal of Community Psychology, 26, 189 206.

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Levine, M, & Perkins, D V (1997) Principles of community psychology: Perspectives and applications (2nd ed) New York: Oxford University Press Levine, M, Reppucci, N D, & Weinstein, R (1990) Learning from Seymour Sarason American Journal of Community Psychology, 18(3), 343 351 Levine, M, Toro, P A, & Perkins, D V (1993) Social and community interventions In Annual review of psychology, Vol 44 (pp 525 558) Palo Alto, CA: Annual Reviews, Inc Levy, L H (2000) Self help groups In J Rappaport & E Seidman (Eds), Handbook of community psychology (pp 591 614) New York: Kluwer Academic/Plenum Press Lewin, K (1951) Field theory in social science: Selected theoretical papers New York: Harper Lindemann, E (1953) The Wellesley project for the study of certain problems in community mental health In Interrelations between the social environment and psychiatric disorders (pp 167 185).

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New York: Milbank Memorial Fund Linney, J A (1986) Court ordered school desegregation: Shuf ing the deck or playing a different game In E Seidman & J Rappaport (Eds), Rede ning social problems (pp 259 274) New York: Plenum Press Linney, J A (2000) Assessing ecological constructs and community context In J Rappaport, & E Seidman (Eds), Handbook of community psychology (pp 647 668) New York: Kluwer Academic/Plenum Press Long, B E (1968) Teaching psychology to children American Psychologist, 23(9), 691 692 Long, B B (1989) The mental health association and prevention Prevention in Human Services, 6(2), 5 44..

To introduce Bayesian networks, we shall use the clothing purchase example, illustrated by the Bayes net in Figure 5.8. Suppose that a clothes retailer operates two outlets, one in New York and one in Los Angeles, each producing sales throughout the four seasons. The retailer is interested in probabilities concerning three articles of clothing in particular: warm coats, business shirts, and Bermuda shorts. Questions of interest include the fabric weight of the article of clothing (light, medium, or heavy) and the color of the article (bright, neutral, or dark). To build a Bayesian network, there are two main considerations: (1) the dependence relationship among the variables of interest, and (2) the associated local probabilities. The retailer has ve variables: season, location, clothing purchase, fabric weight, and color. What is the dependence relationship among these variables For example, does the season of the year depend on the color of the clothes purchased Certainly not, since a customer s purchase of some bright clothing doesn t mean that spring is here, for example, although the customer may wish it so. In fact, the season of the year does not depend on any of the other variables, so we place the node for the variable season at the top of the Bayes network, which indicates that it does not depend on the other variables. Similarly, location does not depend on the other variables, and is therefore placed at the top of the network. Since the fabric weight and the color of the clothing are not known until the article is purchased, the node for the variable clothing purchase is inserted next into the network, with arcs to each of the fabric weight and color nodes.

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