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The main issue in the kernel-based learning methods is to identify the most suitable kernel function. A number of algorithms have been proposed to automatically learn the kernel function/matrix from the labeled examples. However, some of these methods do not assume any parametric form of the kernel functions and interpretability is not possible. Evolutionary algorithms have also been used to evolve the parameter values for a given kernel function. In particular, [41] proposed a parallel evolutionary algorithm to learn optimal parameters in a Gaussian kernel. When Gaussian kernels are used, the resulting SVM corresponds to an RBF network. In this case, hidden neurons and support vectors correspond to each other, thus giving interpretability to the learning machine. 6.4.1.1 Structure Interpretability. The SVM works by mapping the input space into a high-dimensional feature space using a set of nonlinear basis functions [42]. It can be used either in classi cation or regression tasks. The framework was originally designed for pattern recognition, but the basic properties carry over to regression by choosing a suitable cost function, for example, the einsensitive or Vapnik s loss function. Instead of minimizing the empirical training error, like traditional methods, the support vector machine aims at minimizing an upper bound of the generalization error, nding a separating hyperplane and maximizing the margin between the hyperplane and the training data. An SVM with Gaussian kernels belongs to a general class of local model function estimators and can be interpreted as an output of a fuzzy system. However, the SVM solution does not guarantee real model transparency. It guarantees only that the linear combination of local basis functions accurately approximates the true function. The

f I ve done my job, the information provided in this book will be useful to you. It is, however, by no means comprehensive. In addition, new issues tend to crop up, so you ll want to make sure that you re up to date. Therefore, I ve compiled a list of additional resources that may help you become more proficient in Excel application development. I ve classified these resources into three categories: Microsoft technical support Internet newsgroups Internet Web sites

Jokela, M, & Hanin, Y (1999) Does individual zones of optimal functioning model discriminate between successful and less successful athletes A meta-analysis Journal of Sport Sciences, 17, 873 887 Jones, G (1991) Recent developments and current issues in competitive state anxiety research Psychologist, 4, 152 155 Jones, G (1995) Competitive anxiety in sport In S J H Biddle (Ed), European perspectives on exercise and sport psychology (pp 128 153) Leeds, England: Human Kinetics Jones, G, & Hanton, S (2001) Pre-competition feeling states and directional anxiety interpretations Journal of Sports Sciences, 19, 385 395 Kamata, A, Tenenbaum, G, & Hanin, Y L (2002) Individual zone of optimal functioning (IZOF): A probabilistic conceptualization Journal of Sport and Exercise Psychology, 24(2), 189 208 Kerr, J H (1997) Motivation and emotion in sport: Reversal theory East Sussex, England: Psychology Press Koltyn, K F, & Morgan, W P (1997).

If you have a client who wants something different, consider creating a custom cursor using either the client s logo or a tool of the client s trade. For example, if your client is an artist, create a paint brush cursor. If your client is a rock guitarist, create a cursor that looks like the musician s guitar. To create a custom cursor: 1. Choose Insert New Symbol. The Create New Symbol dialog box opens. 2. Name the symbol cursor, choose the Movie Clip behavior, and then click OK to enter symbol-editing mode. 3. Create the object you want to use as a cursor for your design or import a vector graphic or bitmap. 4. Click the Back button to exit symbol-editing mode. 5. Choose Window Library. The document Library opens. 6. Drag an instance of your cursor movie clip onto the Stage. 7. Click the arrow to the left of the word Properties. The Property inspector opens. 8. In the <Instance Name> field, type cursor 9. Select the first frame in your document. 10. Click the arrow to the left of the word Actions. The Actions panel opens. 11. Choose Objects Movie Mouse Methods and then double-click hide. 12. In the left pane of the Actions panel, choose Actions Movie Clip Control and then double-click startDrag. 13. Place your cursor inside the Target field and click the Insert a Target Path button to open the Insert Target Path dialog box.

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Influence of wet suit wear on anxiety responses to underwater exercise Undersea and Hyperbaric Medicine, 24, 23 28 Kuhl, J (1994) A theory of action and state orientations In J Kuhl & J Beckmann ( Eds), Volition and personalit y (pp 9 46) G ttingen, South Lower Saxony, Germany: Hogrefe & Huber Lacey, J I (1967) Somatic patterning and stress: Some revisions of the activation theory In M H Appley & R Trumbell (Eds), Psychological stress (pp 14 37) New York: Appleton-Century-Crofts Landers, D M (1994) Performance, stress, and health: Overall reaction Quest, 46, 123 135 Lane, A, Terry, P, & Karageorghis, L (1995) Antecedents of multidimensional competitive state anxiety and selfconfidence in duathletes Perceptual and Motor Skills, 80, 911 919 Lazarus, R S (1991) Progress on a cognitive-motivationalrelational theory of emotion American Psychologist, 46, 819 834 Lazarus, R S (1993).