Semi-Supervised Learning and Active Learning

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Presentation transcript:

Semi-Supervised Learning and Active Learning 2007/3/20 Semi-Supervised Learning and Active Learning Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Bin Wu

Labeling the examples in the training set L 2007/3/20 Labeling the examples in the training set L Time consuming Tedious Cost consuming Error prone process learn a target concept based on as few as possible labeled examples

semi-supervised learning 2007/3/20 reduce the need for labeled training data semi-supervised learning  active learning

semi-supervised learning 2007/3/20 semi-supervised learning

Semi-supervised Learning 2007/3/20 Labeled data set Unlabeled data set L h Select n (xi, h(xi)) n samples Labeling

Semi-supervised Learning 2007/3/20 Semi-supervised Learning Loop for N iterations Let h be the classifier obtained by training L on L Let MCP be the n examples in U on which h makes the most confident predictions For each x∈MCP do Remove x from U Add <x, h(x)> to L

2007/3/20

Active Learning L h Labeled data set Unlabeled data set Select 2007/3/20 Labeled data set Unlabeled data set L h Select (xi, c(xi)) 1 samples Labeling

Active Learning LOOP for N iterations Selective Sampling 2007/3/20 Active Learning Selective Sampling LOOP for N iterations let h be the classifier obtained by training L on L let remove q from U and ask the user for its label c(q) add <q, c(q)> to L

Common characteristics 2007/3/20 Common characteristics use an additional unlabeled set U try to maximize the accuracy

boosts the accuracy of a supervised 2007/3/20 Semi-supervised Learning boosts the accuracy of a supervised learner based on an additional set of unlabeled examples Active Learning minimizes the amount of labeled data by asking the user to label only the most informative examples

2007/3/20 Reference [1] Learning with labeled and unlabeled data, Matthias Seeger, Institute for Adaptive and Neural ComputationUniversity of Edinburgh, December 19, 2002 [2] Xiaojin Zhu, Semi-Supervised Learning Literature Survey, Computer Sciences, University of Wisconsin-Madiso, 2005, http://www.cs.wisc.edu/$\sim$jerryzhu/pub/ssl\_survey.pdf [3] M. Hasenjäger, Helge Ritter, Active Learning in Neural Networks, http://www.techfak.uni-bielefeld.de/ags/ni/projects/actlearn/ [4] M. Hasenjäger, H. Ritter, Active Learning with Local Models, Neural Processing Letters, 7, 107-117, 1998 [5] Ion Alexandru Muslea, ACTIVE LEARNING WITH MULTIPLE VIEWS, the Degree DOCTOR OF PHILOSOPHY, FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA, December 2002