A spatially explicit, slope-based algorithm was created to delineate MFR zones in 17 arid, mountainous watersheds using elevation and land cover data. Slopes were calculated from elevation data and grouped into classes using iterative self-organizing classification. Land cover types that were inconsistent with groundwater recharge were excluded from the candidate areas to determine the final MFR zones. Slopes and surficial geologic materials that were present in the MFR zones were consistent with conditions at the mountain front, while soils and land cover that were present would generally promote groundwater recharge. Visual inspection of the MFR zone maps also confirmed the presence of well-recognized alluvial fan features in several study watersheds.
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Before electronic computers became available in the fifties , natural pattern recognition capabilities of animals and humans could be tested in psychophysical experiments, but artificial pattern recognition by machines was beyond the state of the art.
This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Preview Unable to display preview. Download preview PDF. Bibliographical Notes M. Anderberg: Cluster Analysis for Applications. Academic Press, New York Google Scholar H. Wiley-Interscience, New York Google Scholar M.
Bongard: Pattern Recognition. Spartan Books, New York-Washington Translated from Russian. Google Scholar R. Duda and P. Hart: Pattern Recognition and Scene Analysis. Wiley, New York Google Scholar K. Fu: Pattern Recognition and Machine Learning.
Proceedings of the Japan-U. Plenum Press, New York-London This book stresses the common aspects of pattern recognition, systems identification and control. Fu: Syntactic Methods in Pattern Recognition. Academic Press, New York-London Fukunaga: Introduction to Statistical Pattern Recognition.
Google Scholar A. Grasselli: Automatic Interpretation and Classification of Images. Google Scholar L. Kanal: Pattern Recognition. Thompson Book Company, Washington, D. Google Scholar W. Minsky and S. Papert: Perceptrons. Google Scholar N. Nilsson: Learning Machines. McGraw-Hill, New York Google Scholar F. Spartan Books, Washington, D. Rosenfeld: Picture Processing by Computer.
Rosenfeld and A. Kak: Digital Picture Processing. Google Scholar G. MacMillan, New York Google Scholar J. Tou and R. Gonzalez: Pattern Recognition Principles. Addison-Wesley, Reading Ullman: Pattern Recognition Techniques. Butterworth, London Watanabe: Knowing and Guessing. Google Scholar S. Watanabe: Frontiers of Pattern Recognition.
Google Scholar T. Winograd: Understanding Natural Language. Young and T. Calvert: Classification, Estimation and and Pattern Recognition.
Google Scholar References 1. Thesis, University of California, Berkeley, April Google Scholar 3. Bassham and M. Kirk: Dynamics of the Photosynthesis of Carbon Compounds. Carboxylation Reactions, Biochem.
Acta 43, —, CrossRef Google Scholar 4. Bassham and G. Acta , —, CrossRef Google Scholar 5. Achievements of High Rates, Plant Sci. Google Scholar 6. Bellman, J. Jacquez, R. Kalaba and S. CrossRef Google Scholar 7. Bellman and R. Kalaba: Quasilinearization and Nonlinear Boundary-value Problems. American Elsevier, New York Google Scholar 8. Blinkov and I.
Plenum Press, New York Google Scholar 9. Braitenberg: Thoughts on the Cerebral Cortex. CrossRef Google Scholar Bremermann: Complexity of Automata, Brains and Behavior. In Physics and Mathematics of the Nervous System, ed. Conrad, W. Google Scholar Quantitative Biology of Metabolism, 3rd Int. Bremermann: Cybernetic Functionals and Fuzzy Sets. Bremermann: Limitations on data processing arising from quantum theory, Part I.
In Optimization through evolution and recombination. In: Self-Organizing Systems, ed. Yovits, G. Jacobi and G. Goldstein, pp. Bremermann: Quantitative aspects of goal-seeking self-organizing systems.
In Progress in Theoretical Biology, Vol. I, ed. Snell, pp.
Pattern recognition principles.
Pattern recognition principles
Pattern recognition principles