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Active Learning: Class Questions

Active Learning: Class Questions. Meeting 10 — Feb 14, 2013 CSCE 6933 Rodney Nielsen. Your Questions. How do you decide what measurements to use for a project. Example: "model loss", "error reduction", F1-score and other measures?

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Active Learning: Class Questions

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  1. Active Learning: Class Questions Meeting 10 — Feb 14, 2013 CSCE 6933 Rodney Nielsen

  2. Your Questions • How do you decide what measurements to use for a project. Example: "model loss", "error reduction", F1-score and other measures? • Is is possible that a given algorithm or approach appears superior simply because of the measurement framework that is chosen?

  3. Your Questions • Can u discuss few scenarios, where cons-bdscan is not applicable? Is there any other approach we can make use of, when that scenario appears?

  4. Your Questions • How is the transitive closures formed?

  5. Your Questions • Are there some real time applications where we use multi label text classification if so can you relate this type of classification to the application and explain how it works?

  6. Your Questions • Can you elaborate on Micro-Average F1 score?

  7. Your Questions • It is mentioned "Informativeness, which is determined by the clustering algorithm’s bias and search preference, refers to the amount of information in the constraints set that the algorithm cannot determine on its own." Is it correct to say that informativeness is analogous to uncertainty sampling?

  8. Your Questions • I am confused about how the paper takes care of outliers. "If an object’s Eps-neighborhood contains no less than MinPts objects, the object is called a core object; otherwise the object is called a border object." Is the cluster containing the border object an outlier? • In algorithm2, step 3, • If the number of objects in neighborhood < MinPts then • Label Point as NOISE temporarily; • Return false; • End If • Does this mean they are treating the outliers as noise?

  9. Your Questions • Effective Multi-Label Active Learning for Text Classification • For the formula 1, I didn't understand well, because I think there was something wrong with it. If it works on the whole distribution, I think it should be: /int /sum( L(fDl)P(y|x)P(x) ), if it works on a interval, it should be P(x) /int_a^b /sum( L(fDl)P(y|x) ).

  10. Your Questions • Effective Multi-Label Active Learning for Text Classification • In the process of predicting number of labels in 4.2.2 section, the first step, how to calculate a probabilities by SVM classifier?

  11. Your Questions • Active Learning of Instance-level Constraints for Semi-supervised Document Clustering. • This paper didn't give much technique details. I want to know how did the semi-supervised concept work in a clustering process?

  12. Your Questions • Active Learning of Instance-level Constraints for Semi-supervised Document Clustering. • I think the overlapping problem can be much different between different two classes. So I don't think just two parameters Eps and MinPts can work well in active learning, how do you think about it?

  13. Your Questions • Are there any clustering validation criteria apart from pairwise f-measure and Normalized mutual information?

  14. Your Questions • Could you please let me know, what is the strategy used in farthest first?

  15. Your Questions • It seems in this paper the author is trying to minimize the human labeling efforts, but in that case, accuracy will also be reduced. So, there could be few scenarios where accuracy is given the first priority, so for that how can we apply this approach..?

  16. Your Questions • In this paper Active learning approach should evaluate each of the unlabeled data at every active learning iteration. But the data here is very large, so this approach could be time consuming and tedious. So how could they overcome this major limitation..?

  17. Your Questions • Effective Multi-Label Active Learning for Text Classification • Multi-label chunking: • Reading the introduction about multi-label classification made me think of IOB chunking. In IOB chunking, how would you handle it if the labels are allowed to overlap? My naive guess is that the best way is to simply create one chunking classifier for each label.

  18. Your Questions • Active Learning of Instance-level Constraints for Semi-supervised Document Clustering • Initial constraints: • How are the initial constraints chosen? Are pairs randomly chosen, or does the initial pool start off with zero constraints?

  19. Your Questions • Active Learning of Instance-level Constraints for Semi-supervised Document Clustering • Minimum number of constraints for total transitive closure: • Is it possible to compute the minimum number of constraints required that would cause transitive closure to assign each object to a cluster? If so, would it be possible to use this information to help evaluate the performance of the algorithm?

  20. Questions • ???

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