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تفاوت induction learning و transduction learning
سلام دوستان توی درس تشخیص الگو کسی تفاوت این دو تا رو خوب متوجه میشه؟
قبلا سر کلاس خونده بودم الان نمیتونم کامل متوجه شم چی بود قضیه
• The Inductive learning algorithm will only have five labeled points to use as a basis for building a predictive model. For example, if a nearest-neighbor algorithm is used, then the points near the middle will be labeled "A" or "C“ instead of “B”.
• Transduction has the advantage of being able to consider all of the points, not just the labeled points, while performing the labeling task. In this case, transductive algorithms would label the unlabeled points according to the clusters to which they naturally belong. The points in the middle, therefore, would most likely be labeled "B",
• One disadvantage of transduction is that it builds no predictive model. If a previously unknown point is added to the set, the entire transductive algorithm would need to be repeated with all of the points in order to predict a label.
اینم تعریفشون از اسلایدهای دکتر ربیعی
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