-
2
-
-
0003223784
-
Multi-label text classification with a mixture model trained by EM
-
A. K. McCallum, Multi-label text classification with a mixture model trained by EM, AAAI 99 Workshop on Text Learning, 1999.
-
(1999)
AAAI 99 Workshop on Text Learning
-
-
McCallum, A.K.1
-
4
-
-
52949141834
-
Decision trees for hierarchical multi-label classification
-
November
-
C. Vens and J. Struyf, Decision Trees for Hierarchical Multi-label Classification, Machine Learning, Volume 73,Issue 2 (November 2008), pp. 185-214.
-
(2008)
Machine Learning
, vol.73
, Issue.2
, pp. 185-214
-
-
Vens, C.1
Struyf, J.2
-
5
-
-
55349142147
-
An empirical study of lazy multilabel classification algorithms
-
E. Spyromitros, G. Tsoumakas and I. Vlahavas, An Empirical Study of Lazy Multilabel Classification Algorithms, in Artificial Intelligence: Theories, Models and Applications, 2008, pp. 401-406.
-
(2008)
Artificial Intelligence: Theories, Models and Applications
, pp. 401-406
-
-
Spyromitros, E.1
Tsoumakas, G.2
Vlahavas, I.3
-
7
-
-
84880083214
-
MMAC: A new multi-class, multi-label associative classification approach
-
F. A. Thabtah, P. Cowling and Y. Peng, MMAC: A New Multi-class, Multi-label Associative Classification Approach, IEEE ICDM, 4 (2004).
-
(2004)
IEEE ICDM
, vol.4
-
-
Thabtah, F.A.1
Cowling, P.2
Peng, Y.3
-
8
-
-
84933515061
-
Multi-label classification: An overview
-
Aristotle University of Thessaloniki, Greece
-
G. Tsoumakas and I. Katakis, Multi-Label Classification: An Overview, Dept. of Informatics, Aristotle University of Thessaloniki, Greece.
-
Dept. of Informatics
-
-
Tsoumakas, G.1
Katakis, I.2
-
14
-
-
52949089060
-
Random k-labelsets: An ensemble method for multilabel classification
-
G. Tsoumakas and I. Vlahavas, Random k-Labelsets: An Ensemble Method for Multilabel Classification, ECML PKDD, 18 (2007).
-
(2007)
ECML PKDD
, vol.18
-
-
Tsoumakas, G.1
Vlahavas, I.2
-
17
-
-
84880106608
-
A pruned problem transformation method for multi-label classification
-
J. Read, A pruned problem transformation method for multi-label classification, NZCSRS, 2008.
-
(2008)
NZCSRS
-
-
Read, J.1
-
18
-
-
0142228873
-
A family of additive online algorithms for category ranking
-
K J.
-
K. Crammer, Y. Singer, J. K, T. Hofmann, T. Poggio and J. Shawetaylor, A Family of Additive Online Algorithms for Category Ranking, in Journal of Machine Learning Research, 3 (2003).
-
(2003)
Journal of Machine Learning Research
, vol.3
-
-
Crammer, K.1
Singer, Y.2
Hofmann, T.3
Poggio, T.4
Shawetaylor, J.5
-
19
-
-
33947681316
-
Ml-knn: A lazy learning approach to multi-label learning
-
M. L. Zhang and Z. H. Zhou, Ml-knn: A lazy learning approach to multi-label learning, in Pattern Recognition, 40 (2007).
-
(2007)
Pattern Recognition
, vol.40
-
-
Zhang, M.L.1
Zhou, Z.H.2
-
20
-
-
33748366796
-
Multi-label neural networks with applications to functional genomics and text categorization
-
M. L. Zhang and Z. H. Zhou, Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization, IEEE Transactions on Knowledge and Data Engineering, 18 (2006), pp. 1338-1351.
-
(2006)
IEEE Transactions on Knowledge and Data Engineering
, vol.18
, pp. 1338-1351
-
-
Zhang, M.L.1
Zhou, Z.H.2
-
21
-
-
3042597440
-
Learning multi-label scene classification
-
M. R. Boutell, J. Luo, X. Shen and C. M. Brown, Learning multi-label scene classification, in Pattern Recognition, 37 (2004), pp. 1757-1771.
-
(2004)
Pattern Recognition
, vol.37
, pp. 1757-1771
-
-
Boutell, M.R.1
Luo, J.2
Shen, X.3
Brown, C.M.4
-
22
-
-
84898954552
-
Parametric mixture models for multi-labeled text
-
N. Ueda and K. Saito, Parametric mixture models for multi-labeled text, NIPS, 15 (2003), pp. 721-728.
-
(2003)
NIPS
, vol.15
, pp. 721-728
-
-
Ueda, N.1
Saito, K.2
-
23
-
-
0033905095
-
BoosTexter: A boosting-based system for text categorization
-
R. E. Schapire and Y. Singer, BoosTexter: A Boosting-based System for Text Categorization, in Machine Learning, 39 (2000), pp. 135-168.
-
(2000)
Machine Learning
, vol.39
, pp. 135-168
-
-
Schapire, R.E.1
Singer, Y.2
-
25
-
-
33646529563
-
StreamMiner: A classifier ensemble-based engine to mine concept-drifting data streams
-
W. Fan, StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams, VLDB, 30 (2004), pp. 1338-1351.
-
(2004)
VLDB
, vol.30
, pp. 1338-1351
-
-
Fan, W.1
-
26
-
-
33749568964
-
A general framework for accurate and fast regression by data summarization in random decision trees
-
W. Fan, J. McCloskey and P. S. Yu, A General Framework for Accurate and Fast Regression by Data Summarization in Random Decision Trees, ACM SIGKDD, 12 (2006), pp. 136-146.
-
(2006)
ACM SIGKDD
, vol.12
, pp. 136-146
-
-
Fan, W.1
McCloskey, J.2
Yu, P.S.3
-
27
-
-
84880112732
-
Is random model better? On its accuracy and efficiency
-
W. Fan, H. Wang, P. S. Yu and S. Ma, Is random model better? On its accuracy and efficiency, IEEE ICDM, 3 (2003).
-
(2003)
IEEE ICDM
, vol.3
-
-
Fan, W.1
Wang, H.2
Yu, P.S.3
Ma, S.4
-
28
-
-
84864028262
-
Multi-instance multi-label learning with application to scene classification
-
Z. H. Zhou and M. L. Zhang, Multi-Instance Multi-Label Learning with Application to Scene Classification, NIPS, 20 (2006), pp. 1609-1616.
-
(2006)
NIPS
, vol.20
, pp. 1609-1616
-
-
Zhou, Z.H.1
Zhang, M.L.2
|