-
3
-
-
84889281816
-
-
Wiley Interscience, New York, NY, USA
-
T. Cover, T. M., and J. A. Thomas. Elements of information theory. Wiley Interscience, New York, NY, USA, 1991.
-
(1991)
Elements of Information Theory
-
-
Cover, T.M.T.1
Thomas, J.A.2
-
7
-
-
0031209604
-
Selective sampling using the query by committee algorithm
-
Y. Freund, S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 2-3:133-168, 1997.
-
(1997)
Machine Learning
, vol.2-3
, pp. 133-168
-
-
Freund, Y.1
Seung, S.2
Shamir, E.3
Tishby, N.4
-
8
-
-
67650566244
-
Crowdsourcing: Why the power of the crowd is driving the future of business
-
J. Howe. Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. Crown Business, 2008.
-
(2008)
Crown Business
-
-
Howe, J.1
-
9
-
-
79952398813
-
CoBayes: Bayesian knowledge corroboration with assessors of unknown areas of expertise
-
G. Kasneci, J. V. Gael, D. Stern, and T. Graepel. CoBayes: Bayesian knowledge corroboration with assessors of unknown areas of expertise. In Conference on Web Search and Data Mining, pages 465-474, 2011.
-
(2011)
Conference on Web Search and Data Mining
, pp. 465-474
-
-
Kasneci, G.1
Gael, J.V.2
Stern, D.3
Graepel, T.4
-
10
-
-
85013879626
-
A sequential algorithm for training text classifiers
-
D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In SIGIR, pages 3-12, 1994.
-
(1994)
SIGIR
, pp. 3-12
-
-
Lewis, D.1
Gale, W.2
-
11
-
-
0001249987
-
On a measure of the information provided by an experiment
-
D. Lindley. On a measure of the information provided by an experiment. Ann. Math. Stat, 27:986-1005, 1956.
-
(1956)
Ann. Math. Stat
, vol.27
, pp. 986-1005
-
-
Lindley, D.1
-
12
-
-
0000695404
-
Information-based objective functions for active data selection
-
D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4:590-604, 1992.
-
(1992)
Neural Computation
, vol.4
, pp. 590-604
-
-
MacKay, D.1
-
13
-
-
84901500766
-
Vuvuzelas and active learning for online classification
-
U. Paquet, J. Van Gael, D. Stern, G. Kasneci, R. Herbrich, and T. Graepel. Vuvuzelas and active learning for online classification. In NIPS Workshop on Comp. Social Science and the Wisdom of Crowds, 2010.
-
(2010)
NIPS Workshop on Comp. Social Science and the Wisdom of Crowds
-
-
Paquet, U.1
Van Gael, J.2
Stern, D.3
Kasneci, G.4
Herbrich, R.5
Graepel, T.6
-
14
-
-
71149084080
-
Supervised learning from multiple experts: Whom to trust when everyone lies a bit
-
V. C. Raykar, S. Yu, L. Zhao, A. Jerebko, C. Florin, G. Hermosillo-Valadez, L. Bogoni, and L. Moy. Supervised learning from multiple experts: Whom to trust when everyone lies a bit. In International Conference on Machine Learning, pages 889-896, 2009.
-
(2009)
International Conference on Machine Learning
, pp. 889-896
-
-
Raykar, V.C.1
Yu, S.2
Zhao, L.3
Jerebko, A.4
Florin, C.5
Hermosillo-Valadez, G.6
Bogoni, L.7
Moy, L.8
-
15
-
-
71149084080
-
Supervised learning from multiple experts: Whom to trust when everyone lies a bit
-
V. C. Raykar, S. Yu, L. Zhao, A. Jerebko, C. Florin, G. Hermosillo-Valadez, L. Bogoni, and L. Moy. Supervised learning from multiple experts: Whom to trust when everyone lies a bit. In Int. Conference on Machine Learning (ICML), pages 889-896, 2009.
-
(2009)
Int. Conference on Machine Learning ICML
, pp. 889-896
-
-
Raykar, V.C.1
Yu, S.2
Zhao, L.3
Jerebko, A.4
Florin, C.5
Hermosillo-Valadez, G.6
Bogoni, L.7
Moy, L.8
-
16
-
-
0442319140
-
Toward optimal active learning through sampling estimation of error reduction
-
N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In 18th International Conference on Machine Learning, pages 444-448, 2001.
-
(2001)
18th International Conference on Machine Learning
, pp. 444-448
-
-
Roy, N.1
McCallum, A.2
-
17
-
-
67049158342
-
How to get the most out of your curation effort
-
A. Rzhetsky, H. Shatkay, and W. J. Wilbur. How to get the most out of your curation effort. PLoS Computational Biology, 5(5):e1000391, 2009.
-
(2009)
PLoS Computational Biology
, vol.5
, Issue.5
, pp. e1000391
-
-
Rzhetsky, A.1
Shatkay, H.2
Wilbur, W.J.3
-
20
-
-
65449144451
-
Get another label? Improving data quality and data mining using multiple, noisy labelers
-
V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? Improving data quality and data mining using multiple, noisy labelers. In Knowledge Discovery and Data Mining (KDD), pages 614-622, 2008.
-
(2008)
Knowledge Discovery and Data Mining (KDD)
, pp. 614-622
-
-
Sheng, V.S.1
Provost, F.2
Ipeirotis, P.G.3
-
21
-
-
85153964878
-
Inferring ground truth from subjective labeling of Venus images
-
P. Smyth, U. Fayyad, M. Burl, P. Perona, and P. Baldi. Inferring ground truth from subjective labeling of Venus images. In Advances in Neural Information Processing Systems, volume 7, pages 1085-1092, 1995.
-
(1995)
Advances in Neural Information Processing Systems
, vol.7
, pp. 1085-1092
-
-
Smyth, P.1
Fayyad, U.2
Burl, M.3
Perona, P.4
Baldi, P.5
-
24
-
-
80052400610
-
Modeling annotator expertise: Learning when everybody knows a bit of something
-
Y. Yan, R. Rosales, G. Fung, M. Schmidt, G. Hermosillo, L. Bogoni, L. Moy, and J. Dy. Modeling annotator expertise: Learning when everybody knows a bit of something. In International Conference on Artificial Intelligence and Statistics, pages 932-939, 2010.
-
(2010)
International Conference on Artificial Intelligence and Statistics
, pp. 932-939
-
-
Yan, Y.1
Rosales, R.2
Fung, G.3
Schmidt, M.4
Hermosillo, G.5
Bogoni, L.6
Moy, L.7
Dy, J.8
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