Concept Learning - 计算机辅助设计与图形学.ppt
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1、1,Concept Learning,By Dong XuState Key Lab of CAD&CG, ZJU,2,Overview,IntroductionPerspectiveAlgorithmsRemarksInductive BiasConclusions,3,Introduction,What is concept learning?Induce boolean function from a sample of positive/negative training examples.Concept learning in daily life根据人证物证判断犯罪嫌疑人是否有罪根
2、据笔试面试决定是否录用Any more ?,4,A Demo Task EnjoySport,Given:Instances X: Possible days, each described by the attributesSky (Sunny, Cloudy, and Rainy)Temp (Warm and Cold)Humidity (Normal and High)Wind (Strong and Weak)Water (Warm and Cool)Forecast (Same and Change)Hypotheses H: Each hypothesis is described
3、 by a conjunction of constraints. These constraints may be “?” (any value), “0” (no value), or a specific value.Determine:A hypothesis h in H such that h(x) = c(x) for all x in X.,5,EnjoySport Training Data,6,The Inductive Learning Hypothesis,Any hypothesis found to approximate the target function w
4、ell over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples.根据已知推断未知,假定已知满足某种规律,7,Perspective,Concept learning can be formulated as a searching - through a predefined space of potential hypotheses for the hypothesis that best f
5、its the training examples.General-to-specific orderingExample : = Introduce a hierarchy structure into hypotheses space, which leads to efficient searching strategy.,8,Algorithms,9,FIND-S,h0 = h1 = h2 = h3 = h4 = Training examples:, Enjoy Sport = Yes, Enjoy Sport = Yes, Enjoy Sport = No, Enjoy Sport
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- concept learning 计算机辅助设计 图形学
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