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THERAPISTS REPORTED LEVELS OF COMFORT Further results of the survey suggested a decrease from 1986 to 1992 in the number of respondents who reported feeling completely comfortable in working with any ethnic group. The percent of respondents reporting feeling completely comfortable with white clients decreased from 1986 to 1992, 88.8% to 85.2%, respectively. Therapists with Native American clients showed the least comfort with that population. The shifts in reported levels of comfort from 1986 to 1992 suggested that both white and ethnic minority therapists were moving away from generalized views of all clients being inherently the same and were more closely scrutinizing themselves, their abilities, and their preparation for clinical work with clients from various ethnic and cultural groups. The release of the 1990 APA Guidelines for Providers of Psychological Services to Ethnic, Linguistic, and Culturally Diverse Populations challenged psychologists level of knowledge and skill in working with the ethnically diverse, thus creating uncertainties and discomfort. Even though respondents reported higher levels of discomfort, they were in fact becoming better prepared as they anticipated ethnic and cultural issues in their clinical work, and the numerous considerations required for applying a sociocultural framework to diagnosis, assessment, and treatment of the culturally diverse.

That is to say, chunk size does not affect the nal classi cation accuracy of the system, and the learning time is signi cantly shortened depending on the chunk size Furthermore, we examined the approximation error of the eigenvectors obtained by Chunk IPCA As a result, Chunk IPCA learned major eigenvectors without serious errors, whereas minor eigenvectors have large errors However, as far as we can see from the experimental results, the errors for minor eigenvectors do not seriously affect classi cation accuracy There remains further work to be done First, the approximation error to minor eigenvectors may prevent construction of an effective eigenspace if the sample distributions were largely changed over time This problem can be alleviated by introducing an adaptive selection for the threshold q Second, system performance can be greatly enhanced by introducing Incremental Linear Discriminant Analysis (LDA) (Pang et al.

Landers, D M (1999) The influence of exercise and mental health In C B Corbin & R P Pangrazi (Eds), Toward a better understanding of physical fitness and activity (pp 137 143) Scottsdale, AZ: Holcomb Hathaway Landers, D M, & Alderman, B (in press) Exercise relative to other treatments for reduction of anxiety/depression: Overcoming the principle of least effort Promoting Health and Performance for Life: Invited Papers from the 11th World Congress of Sport Psychology Landers, D M, & Arent, S M (2001) Exercise and mental health In R N Singer, H A Hausenblas, & C M Janelle (Eds), Handbook of sport psychology (2nd, ed, pp 740 765) New York: Wiley Landers, D M, & Petruzzello, S J (1994) Physical activity, fitness, and anxiety In C Bouchard, R J Shephard, & T Stevens ( Eds), Physical activit y, fitness, and health (pp 868 882).

Champaign, IL: Human Kinetics Lawlor, D A, & Hopker, S W (2001) The effectiveness of exercise as an intervention in the management of depression: Systematic review and meta-regression analysis of randomized controlled trials British Medical Journal, 322, 763 777 Lazarov, O, Robinson, J, Tang, Y P, Hairston, H S, KoradeMirnics, Z, Lee, V M-Y, et al (2005) Environmental enrichment reduces A levels and amyloid deposition in transgenic mice Cell, 120(5), 701 713 Lazarus, R S (1991) Progress on a cognitive-motivationalrelational theory of emotion American Psychologist, 46, 819 834 Lazarus, R S, & Cohen, J P (1977) Environmental stress In I Altman & J F Wohlwill (Eds), Human behavior and the environment: Current theory and research (pp 89 127) New York: Plenum Press Leith, L M (1994) Foundations of exercise and mental health Morgantown, WV: Fitness Information Technology Linthorst, A C.

, 2005) as feature extraction and by introducing other powerful classi ers such as neural networks (Ozawa et al, 2005) and a support vector machine Therefore, pursuing the optimal combination of feature space learning and classi er learning is another interesting topic Finally, PCA and LDA transform inputs into linear features, and these features are not always effective for classi cation purposes Recently, kernel PCA and kernel LDA have been widely noticed as high-performance feature extraction methods (Scholkopf et al, 1996); hence, the extension of the incremental learning approach to kernel PCA (Takeuchi et al, 2007) and kernel LDA (Cai et al, 2007) is also a promising future work..