-
2
-
-
0002993682
-
Combining labeled & unlabeled data with co-training
-
A. Blum & T. Mitchell. Combining labeled & unlabeled data with co-training. COLT-98.
-
COLT-98
-
-
Blum, A.1
Mitchell, T.2
-
3
-
-
9444244214
-
Exploiting relations among concepts to acquire weakly labeled training data
-
J. Bockhorst & M. Craven. Exploiting relations among concepts to acquire weakly labeled training data. ICML-2002, 2002.
-
(2002)
ICML-2002
-
-
Bockhorst, J.1
Craven, M.2
-
4
-
-
84944949595
-
The effect of adding relevance information in a relevance feedback environment
-
C. Buckley, G. Salton, and J. Allan. The effect of adding relevance information in a relevance feedback environment. S1GIR-94, 1994.
-
(1994)
S1GIR-94
-
-
Buckley, C.1
Salton, G.2
Allan, J.3
-
6
-
-
84961317343
-
PAC learning from positive statistical queries
-
F. Denis. PAC learning from positive statistical queries. ALT-98, pages 112-126. 1998.
-
(1998)
ALT-98
, pp. 112-126
-
-
Denis, F.1
-
7
-
-
84880793807
-
Enhancing supervised learning with unlabeled data
-
S. Goldman & Y. Zhou. Enhancing supervised learning with unlabeled data. ICML-2000.
-
ICML-2000
-
-
Goldman, S.1
Zhou, Y.2
-
8
-
-
0002714543
-
Making large-Scale SVM Learning Practical
-
B. Schftlkopf and C. Burges and A. Smola (ed.), MIT-Press
-
T. Joachims. Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Schftlkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999
-
(1999)
Advances in Kernel Methods - Support Vector Learning
-
-
Joachims, T.1
-
9
-
-
0032202014
-
Efficient noise-tolerant learning from statistical queries
-
M. Kearns. Efficient noise-tolerant learning from statistical queries. J. of the ACM,45:983-1006, 1998.
-
(1998)
J. of the ACM
, vol.45
, pp. 983-1006
-
-
Kearns, M.1
-
11
-
-
85013879626
-
A sequential algorithm for training text classifiers
-
D. Lewis & W. Gale. A sequential algorithm for training text classifiers. SIGIR-94, 1994.
-
(1994)
SIGIR-94
-
-
Lewis, D.1
Gale, W.2
-
12
-
-
0742311711
-
Partially supervised classification of text documents
-
B. Liu, W. Lee, P. Yu, & X. Li. Partially supervised classification of text documents. ICML-2002.
-
ICML-2002
-
-
Liu, B.1
Lee, W.2
Yu, P.3
Li, X.4
-
13
-
-
3242788638
-
Active + semi-supervised learning = robust multi-view learning
-
I. Muslea, S. Minton & C. Knoblock. Active + semi-supervised learning = robust multi-view learning. ICML-2002, 2002.
-
(2002)
ICML-2002
-
-
Muslea, I.1
Minton, S.2
Knoblock, C.3
-
14
-
-
1942514815
-
Learning from the positive data
-
to appear
-
S. Muggleton. Learning from the positive data. Machine Learning, 2001, to appear.
-
(2001)
Machine Learning
-
-
Muggleton, S.1
-
18
-
-
0038091288
-
-
Technical Report MSR-TR-99-87, Microsoft Research
-
B. Scholkopf, J. Piatt, J. Shawe, A. Smola & R. Williamson. Estimating the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research, 1999.
-
(1999)
Estimating the Support of a High-Dimensional Distribution
-
-
Scholkopf, B.1
Piatt, J.2
Shawe, J.3
Smola, A.4
Williamson, R.5
-
20
-
-
84880818418
-
A re-examination of text categorization methods
-
Y. Yang & X. Liu. A re-examination of text categorization methods. S1G1R-99, 1999.
-
(1999)
S1G1R-99
-
-
Yang, Y.1
Liu, X.2
-
21
-
-
0011399035
-
PEBL: Positive example based learning for Web page classification using SVM
-
H. Yu, J. Han, & K. Chang. PEBL: Positive example based learning for Web page classification using SVM. KDD-02, 2002.
-
(2002)
KDD-02
-
-
Yu, H.1
Han, J.2
Chang, K.3
|