: Active Learning 2017/11/12

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1 : Active Learning 2017/11/12

2 Contents 0.1 Introduction Membership Query Synthesis Stream-based Selective Sampling Pool-based Sampling Uncertainty Sampling Query-By-Committee Epected Model Change Epected Error Reduction Batch-Mode Active Learning Noisy Oracles Variable Labeling Costs Multi-Task Active Learning Stopping Criteria Semi-Supervised Learning Submodular Optimization Graph Structure

3 0.1 Introduction ( ) 0.2 [16] 3 Membership Query Synthesis Stream-based Selective Sampling Pool-based Sampling Membership Query Synthesis membership queries[1] Figure 1: 2

4 [3] Stream-based Selective Sampling selective sampling[2] selective sampling membership query Figure 2: [6] [5] Pool-based Sampling Figure3 Figure 3: 3

5 L U 0.3 A A Uncertainty Sampling uncertainty sampling LG = arg ma 1 P θ (ŷ ) (1) ŷ θ 1 margin sampling M = arg min P θ (ŷ 1 ) P θ (ŷ 2 ) (2) ŷ 1 ŷ 2 2 margin sampling 2 margin sampling uncertainty sampling H = arg ma i P θ (y i ) log P θ (y i ) (3) y i Query-By-Committee query-by-committee (QBC) [19] QBC committee C = {θ (1),..., θ (C) } committee 2 [6] 4

6 V E = arg ma i V (y i ) C log V (y i) C (4) y i V (y i ) committee C committee Kullback-Leibler (KL) divergence [13] KL = arg ma 1 C C D(P θ (c) P C ) (5) c=1 D(P θ (c) P C ) = i P θ (c)(y i ) log P θ (c)(y i ) P C (y i ) (6) θ (c) committee C committee Epected Model Change epective gradient length (EGL) [18] EGL l θ (L) l l θ (L, y ), y y EGL = arg ma P θ (y i ) l θ (L, y i ) (7) i Epected Error Reduction 0/1 0/1 = arg min ( U P θ (y i ) i 1 P θ +,y i (ŷ (u) ) u=1 θ +,y i, y i L epected error reduction ) (8) 5

7 Batch-Mode Active Learning Q Q best [4][10][11] Noisy Oracles Variable Labeling Costs [17] Multi-Task Active Learning Reichart [15] 2 6

8 0.4.5 Stopping Criteria Semi-Supervised Learning [20][14] Submodular Optimization [9][8] Graph Structure [12][7] 7

9 Bibliography [1] Dana Angluin. Queries and concept learning. Machine learning, 2(4): , [2] Les E Atlas, David A Cohn, and Richard E Ladner. Training connectionist networks with queries and selective sampling. In Advances in neural information processing systems, pages , [3] Eric B Baum and Kenneth Lang. Query learning can work poorly when a human oracle is used. In International joint conference on neural networks, volume 8, page 8, [4] Klaus Brinker. Incorporating diversity in active learning with support vector machines. In Proceedings of the 20th International Conference on Machine Learning (ICML-03), pages 59 66, [5] David Cohn, Les Atlas, and Richard Ladner. Improving generalization with active learning. Machine learning, 15(2): , [6] Ido Dagan and Sean P Engelson. Committee-based sampling for training probabilistic classifiers. In Proceedings of the Twelfth International Conference on Machine Learning, pages The Morgan Kaufmann series in machine learning,(san Francisco, CA, USA), [7] Gautam Dasarathy, Robert Nowak, and Xiaojin Zhu. S2: An efficient graph based active learning algorithm with application to nonparametric classification. In Conference on Learning Theory, pages , [8] Daniel Golovin and Andreas Krause. Adaptive submodularity: A new approach to active learning and stochastic optimization. In COLT, pages , [9] Daniel Golovin and Andreas Krause. Adaptive submodularity: Theory and applications in active learning and stochastic optimization. Journal of Artificial Intelligence Research, 42: , [10] Yuhong Guo and Dale Schuurmans. Discriminative batch mode active learning. In Advances in neural information processing systems, pages , [11] Steven CH Hoi, Rong Jin, Jianke Zhu, and Michael R Lyu. Batch mode active learning and its application to medical image classification. In Proceedings of the 23rd international conference on Machine learning, pages ACM, [12] Kwang-Sung Jun and Robert Nowak. Graph-based active learning: A new look at epected error minimization. In Signal and Information Processing (GlobalSIP), 2016 IEEE Global Conference on, pages IEEE,

10 [13] Andrew Kachites McCallumzy and Kamal Nigamy. Employing em and pool-based active learning for tet classification. In Proc. International Conference on Machine Learning (ICML), pages Citeseer, [14] Ion Muslea, Steven Minton, and Craig A Knoblock. Active+ semi-supervised learning= robust multi-view learning. In ICML, volume 2, pages , [15] Roi Reichart, Katrin Tomanek, Udo Hahn, and Ari Rappoport. Multi-task active learning for linguistic annotations. In ACL, volume 8, pages , [16] Burr Settles. Active learning literature survey. University of Wisconsin, Madison, 52(55-66):11, [17] Burr Settles, Mark Craven, and Lewis Friedland. Active learning with real annotation costs. In Proceedings of the NIPS workshop on cost-sensitive learning, pages 1 10, [18] Burr Settles, Mark Craven, and Soumya Ray. Multiple-instance active learning. In Advances in neural information processing systems, pages , [19] H Sebastian Seung, Manfred Opper, and Haim Sompolinsky. Query by committee. In Proceedings of the fifth annual workshop on Computational learning theory, pages ACM, [20] Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani. Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In ICML 2003 workshop on the continuum from labeled to unlabeled data in machine learning and data mining, volume 3,

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