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37 O V Lepskij, On a Problem of Adaptive Estimation in Gaussian White Noise, Theory of Probability and its Applications, 35 (3): 454 466, 1990 38 E Lima, F Gomide, R Ballini, Participatory Evolving Fuzzy Modeling Second Int Symposium on Evolving Fuzzy Systems, Lake District, UK, 2006, pp 36 41 39 L Ljung, System Identi cation: Theory for the User Upper Saddle River, NJ: Prentice Hall PTR, Prentice-Hall, 1999 40 E Lughofer, Extensions of Vector Quantization for Incremental Clustering, Pattern Recognition, 41 (3): 995 1011, 2008 41 E Lughofer, FLEXFIS: A Robust Incremental Learning Approach for Evolving TS Fuzzy Models, IEEE Trans on Fuzzy Systems (Special Issue On Evolving Fuzzy Systems), 16 (4): 1 18, 2008 42 E Lughofer, P Angelov, X Zhou, Evolving Single- and Multi-Model Fuzzy Classi ers with FLEXFIS-Class Proceedings of FUZZ-IEEE 2007, London, UK, 2007 pp 363 368 43 E Lughofer, C.

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Guardiola, Applying Evolving Fuzzy Models with Adaptive Local Error Bars to On-line Fault Detection Proceedings of Genetic and Evolving Fuzzy Systems 2008, Witten-Bommerholz, Germany, 2008, pp 35 40 44 E Lughofer, C Guardiola, On-line Fault Detection with Data-Driven Evolving Fuzzy Models, Journal of Control and Intelligent Systems, 36 (2), 2008 45 E Lughofer, E H llermeier, E P Klement, Improving the Interpretability of Data-Driven u Evolving Fuzzy Systems Proceedings of EUSFLAT 2005, Barcelona, Spain, 2005, pp 28 33 46 J G Qiang Shen Marin-Blazquez, From Approximative to Descriptive Fuzzy Classi ers, IEEE Transactions on Fuzzy Systems, 10 (4): 484 497, 2002 47 V A Morozov, On the Solution of Functional Equations by the Methdod of Regularization, Sov Math Dokl, 7: 414 417, 1965 48 D Nauck, R Kruse, A Neuro-Fuzzy Method to Learn Fuzzy Classi cation Rules from Data, Fuzzy Sets and Systems, 89(3): 277 288, 1997 49 A.

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Piegat, Fuzzy Modeling and Control Heidelberg, New York: Physica Verlag, SpringerVerlag, 2001 50 F Provost, Machine Learning from Imbalanced Data Sets Proceedings of the AAAI workshop, Menlo Park, CA, 2000, pp 1 3 51 S J Qin, W Li, H H Yue, Recursive PCA for Adaptive Process Monitoring, Journal of Process Control, 10: 471 486, 2000 52 J A Roubos, M Setnes, J Abonyi, Learning Fuzzy Classi cation Rules from Data, Information Sciences Informatics and Computer Science: An International Journal, 150: 77 93, 2003 53 D Donoho, S Chen, M Saunders, Atomic decomposition by basis pursuit, SIAM Rev, 43 (1): 129 159, 2001 54 D Sannen, M Nuttin, J E Smith, M A Tahir, E Lughofer, C Eitzinger, An Interactive SelfAdaptive On-line Image Classi cation Framework In, Proceedings of ICVS 2008, Vol 5008 of LNCS ( A Gasteratos, M Vincze, J K Tsotsos, ed.

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You would need to open two No. 10 cans, and there would be some product left over. Another scenario exists with canned produce. The yield on canned tomato paste, tomato puree, or crushed tomatoes is 100 percent. These canned items are used as ingredients in a recipe. Therefore, the cost per can times the number of cans used is added to the cost for all of the ingredients in the recipe. For example, crushed tomatoes are purchased in No. 10 cans. There are six No. 10 cans per case. A case cost $12.00. Each can costs $12.00 (cost per case) / 6 (cans per case) $2.00 (cost per can)

After all this explanation of pixels and resolution, you may still be wondering what the best resolution would be for any image images that will be printed, displayed onscreen, or both The answer is a frustrating non-answer to some and a liberating whatever you want! to others: The resolution is entirely up to you, and there s no hard and fast rule You re best off using the highest resolution possible, of course, because the higher the resolution, the cleaner, crisper, and more detailed the image is, but there are no absolute right answers The images in this book vary from 150 ppi for figures that show black-and-white images, dialog boxes, and other parts of the workspace to 300 ppi for the images that appear in color.

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