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Combinatorics of Experimental Design (Oxford Science Publications) by Anne Penfold Street (Repost)
Electrochemical Science and Technology - Fundamentals and Applications
"Modern Techniques in Neuroscience Research" by ed. Uwe Windhorst, Hakan Johansson (Repost)
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Science and Technology Ethics [Repost]
Sustainable Energy Pricing - Nature, Sustainable Engineering, and the Science of Energy Pricing
Glycoscience: Synthesis of Substrate Analogs and Mimetics
Service Science, Management, and Engineering Education for the 21st Century
Big Ecology - The Emergence of Ecosystem Science
Happiness - The Science behind Your Smile
-Service Science, Management, and Engineering Education for the 21st Century- ed. by Bill Hefley and Wendy Murphy (Repost)
Calculus for Business, Economics, Life Sciences and Social Sciences (12th Edition)
Science and Engineering Indicators 2010
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Demand-Driven Associative Classification (SpringerBriefs in Computer Science)
Posted on 2011-05-30
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More Adriano Veloso, "" The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance. PLEASE INFORM ME IF LINKS ARE DEAD
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