-
1
-
-
0034172483
-
Learning to construct knowledge bases from the World Wide Web
-
Craven M., DiPasquoa D., Freitagb D., McCalluma A., Mitchella T., Nigama K., and Slatterya S. Learning to construct knowledge bases from the World Wide Web. Artificial Intelligence 118 1-2 (2000) 69-113
-
(2000)
Artificial Intelligence
, vol.118
, Issue.1-2
, pp. 69-113
-
-
Craven, M.1
DiPasquoa, D.2
Freitagb, D.3
McCalluma, A.4
Mitchella, T.5
Nigama, K.6
Slatterya, S.7
-
2
-
-
0141771188
-
A survey of methods for scaling up inductive learning algorithms
-
Provost F.J., and Kolluri V. A survey of methods for scaling up inductive learning algorithms. Data Mining and Knowledge Discovery 2 (1999) 131-169
-
(1999)
Data Mining and Knowledge Discovery
, vol.2
, pp. 131-169
-
-
Provost, F.J.1
Kolluri, V.2
-
3
-
-
4644347255
-
A selective sampling approach to active feature selection
-
Liu H., Motada H., and Yu L. A selective sampling approach to active feature selection. Artificial Intelligence 159 1-2 (2004) 49-74
-
(2004)
Artificial Intelligence
, vol.159
, Issue.1-2
, pp. 49-74
-
-
Liu, H.1
Motada, H.2
Yu, L.3
-
4
-
-
0347763609
-
Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study
-
Cano J.R., Herrera F., and Lozano M. Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study. IEEE Transactions on Evolutionary Computation 7 6 (2003) 561-575
-
(2003)
IEEE Transactions on Evolutionary Computation
, vol.7
, Issue.6
, pp. 561-575
-
-
Cano, J.R.1
Herrera, F.2
Lozano, M.3
-
5
-
-
0036104537
-
Advances in instance selection for instance-based learning algorithms
-
Brighton H., and Mellish C. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6 (2002) 153-172
-
(2002)
Data Mining and Knowledge Discovery
, vol.6
, pp. 153-172
-
-
Brighton, H.1
Mellish, C.2
-
6
-
-
33845982223
-
Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability
-
Cano J.R., Herrera F., and Lozano M. Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. Data & Knowledge Engineering 60 1 (2007) 90-108
-
(2007)
Data & Knowledge Engineering
, vol.60
, Issue.1
, pp. 90-108
-
-
Cano, J.R.1
Herrera, F.2
Lozano, M.3
-
9
-
-
0032280519
-
Boosting the margin: A new explanation for the effectiveness of voting methods
-
Schapire R.E., Freund Y., Bartlett P.L., and Lee W.S. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics 26 5 (1998) 1651-1686
-
(1998)
Annals of Statistics
, vol.26
, Issue.5
, pp. 1651-1686
-
-
Schapire, R.E.1
Freund, Y.2
Bartlett, P.L.3
Lee, W.S.4
-
10
-
-
21744434575
-
Regaining sparsity in kernel principal components
-
García-Osorio C., and Fyfe C. Regaining sparsity in kernel principal components. Neurocomputing 67 (2005) 398-402
-
(2005)
Neurocomputing
, vol.67
, pp. 398-402
-
-
García-Osorio, C.1
Fyfe, C.2
-
12
-
-
0002230256
-
Grand Tour methods: An outline
-
Allen D. (Ed), Elsevier Science Publisher B.V., North Holland, Amsterdam
-
Buja A., and Asimov D. Grand Tour methods: An outline. In: Allen D. (Ed). Computer Science and Statistics: Proceedings of the Seventeenth Symposium on the Interface (1986), Elsevier Science Publisher B.V., North Holland, Amsterdam 63-67
-
(1986)
Computer Science and Statistics: Proceedings of the Seventeenth Symposium on the Interface
, pp. 63-67
-
-
Buja, A.1
Asimov, D.2
-
13
-
-
67649637661
-
-
North-Holland Publishing Co. Ch. 14, pp. 391-414
-
Buja A., Cook D., Asimov D., and Hurley C. Computational Methods for High-Dimensional Rotations in Data Visualization (2005), North-Holland Publishing Co. Ch. 14, pp. 391-414
-
(2005)
Computational Methods for High-Dimensional Rotations in Data Visualization
-
-
Buja, A.1
Cook, D.2
Asimov, D.3
Hurley, C.4
-
15
-
-
0000974551
-
Plots of high dimensional data
-
Andrews D.F. Plots of high dimensional data. Biometrics 28 (1972) 125-136
-
(1972)
Biometrics
, vol.28
, pp. 125-136
-
-
Andrews, D.F.1
-
16
-
-
11144306415
-
Three-dimensional Andrews plots and the grand tour
-
Wegman E.J., and Shen J. Three-dimensional Andrews plots and the grand tour. Computing Science and Statistics 25 (1993) 284-288
-
(1993)
Computing Science and Statistics
, vol.25
, pp. 284-288
-
-
Wegman, E.J.1
Shen, J.2
-
17
-
-
76549132784
-
A cooperative coevolutionary algorithm for instance selection for instance-based learning
-
García-Pedrajas N., Romero del Castillo J.A., and Ortiz-Boyer D. A cooperative coevolutionary algorithm for instance selection for instance-based learning. Machine Learning 78 3 (2010) 381-420
-
(2010)
Machine Learning
, vol.78
, Issue.3
, pp. 381-420
-
-
García-Pedrajas, N.1
Romero del Castillo, J.A.2
Ortiz-Boyer, D.3
-
18
-
-
17444379003
-
Stratification for scaling up evolutionary prototype selection
-
Cano J.R., Herrera F., and Lozano M. Stratification for scaling up evolutionary prototype selection. Pattern Recognition Letters 26 7 (2005) 953-963
-
(2005)
Pattern Recognition Letters
, vol.26
, Issue.7
, pp. 953-963
-
-
Cano, J.R.1
Herrera, F.2
Lozano, M.3
-
19
-
-
2942516120
-
Enhancing prototype reduction schemes with recursion: A method applicable for "large" data sets
-
Kim S.-W., and Oommen B.J. Enhancing prototype reduction schemes with recursion: A method applicable for "large" data sets. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 34 3 (2004) 1384-1397
-
(2004)
IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics
, vol.34
, Issue.3
, pp. 1384-1397
-
-
Kim, S.-W.1
Oommen, B.J.2
-
20
-
-
65049087517
-
A divide-and-conquer recursive approach for scaling up instance selection algorithms
-
de Haro-García A., and Pedrajas N.G. A divide-and-conquer recursive approach for scaling up instance selection algorithms. Data Mining and Knowledge Discovery 18 3 (2009) 392-418
-
(2009)
Data Mining and Knowledge Discovery
, vol.18
, Issue.3
, pp. 392-418
-
-
de Haro-García, A.1
Pedrajas, N.G.2
-
24
-
-
0242540431
-
Mining complex models from arbitrarily large databases in constant time
-
Edmonton, Canada
-
G. Hulten, P. Domingos, Mining complex models from arbitrarily large databases in constant time, in: Proceedings of the International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, 2002, pp. 525-531
-
(2002)
Proceedings of the International Conference on Knowledge Discovery and Data Mining
, pp. 525-531
-
-
Hulten, G.1
Domingos, P.2
-
26
-
-
29644438050
-
Statistical comparisons of classifiers over multiple data sets
-
Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1-30
-
-
Demšar, J.1
-
28
-
-
0343081513
-
Reduction techniques for instance-based learning algorithms
-
Wilson D.R., and Martinez T.R. Reduction techniques for instance-based learning algorithms. Machine Learning 38 (2000) 257-286
-
(2000)
Machine Learning
, vol.38
, pp. 257-286
-
-
Wilson, D.R.1
Martinez, T.R.2
-
29
-
-
0015361129
-
Asymptotic properties of nearest neighbor rules using edited data
-
Wilson D.L. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2 3 (1972) 408-421
-
(1972)
IEEE Transactions on Systems, Man, and Cybernetics
, vol.2
, Issue.3
, pp. 408-421
-
-
Wilson, D.L.1
-
32
-
-
0000935031
-
Editing for the k-nearest neighbors rule by a genetic algorithm
-
Kuncheva L. Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognition Letters 16 (1995) 809-814
-
(1995)
Pattern Recognition Letters
, vol.16
, pp. 809-814
-
-
Kuncheva, L.1
-
34
-
-
0346238443
-
Using genetic algorithms for training data selection in RBF networks
-
Liu H., and Motoda H. (Eds), Kluwer, Norwell, Massachusetts, USA
-
Reeves C.R., and Bush D.R. Using genetic algorithms for training data selection in RBF networks. In: Liu H., and Motoda H. (Eds). Instances Selection and Construction for Data Mining (2001), Kluwer, Norwell, Massachusetts, USA 339-356
-
(2001)
Instances Selection and Construction for Data Mining
, pp. 339-356
-
-
Reeves, C.R.1
Bush, D.R.2
-
36
-
-
76549114537
-
-
D. Whitley, The GENITOR algorithm and selective pressure, in: M.K. Publishers (Ed.), Proc. 3rd International Conf. on Genetic Algorithms, Los Altos, CA, 1989, pp. 116-121
-
D. Whitley, The GENITOR algorithm and selective pressure, in: M.K. Publishers (Ed.), Proc. 3rd International Conf. on Genetic Algorithms, Los Altos, CA, 1989, pp. 116-121
-
-
-
-
38
-
-
0003984832
-
Population-based incremental learning
-
Tech. Rep. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh
-
S. Baluja, Population-based incremental learning, Tech. Rep. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, 1994
-
(1994)
-
-
Baluja, S.1
-
39
-
-
70349334855
-
Disturbing neighbors diversity for decision forest
-
G. Valentini, O. Okun Eds, Patras, Greece
-
J. Maudes-Raedo, J.J. Rodríguez-Díez, C. García-Osorio, Disturbing neighbors diversity for decision forest, in: G. Valentini, O. Okun (Eds.), Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA 2008), Patras, Greece, 2008, pp. 67-71
-
(2008)
Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA
, pp. 67-71
-
-
Maudes-Raedo, J.1
Rodríguez-Díez, J.J.2
García-Osorio, C.3
-
40
-
-
0008181958
-
Impact of learning set quality and size on decision tree performances, International Journal of Computers
-
Sebban M., Nock R., Chauchat J.H., and Rakotomalala R. Impact of learning set quality and size on decision tree performances, International Journal of Computers. Systems and Signals 1 1 (2000) 85-105
-
(2000)
Systems and Signals
, vol.1
, Issue.1
, pp. 85-105
-
-
Sebban, M.1
Nock, R.2
Chauchat, J.H.3
Rakotomalala, R.4
-
44
-
-
76549111507
-
Identifying and eliminating irrelevant instances using information theory
-
13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000. Hamilton H., and Yang Q. (Eds). Montreal, Springer
-
Sebban M., and Nock R. Identifying and eliminating irrelevant instances using information theory. In: Hamilton H., and Yang Q. (Eds). 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000. Montreal. Lecture Notes in Artificial Intelligence vol. 1822 (2000), Springer 90-101
-
(2000)
Lecture Notes in Artificial Intelligence
, vol.1822
, pp. 90-101
-
-
Sebban, M.1
Nock, R.2
-
46
-
-
0032645080
-
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
-
Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 1/2 (1999) 105-142
-
(1999)
Machine Learning
, vol.36
, Issue.1-2
, pp. 105-142
-
-
Bauer, E.1
Kohavi, R.2
-
47
-
-
0034250160
-
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
-
Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 (2000) 139-157
-
(2000)
Machine Learning
, vol.40
, pp. 139-157
-
-
Dietterich, T.G.1
-
48
-
-
60849105643
-
Constructing ensembles of classifiers by means of weighted instance selection
-
García-Pedrajas N. Constructing ensembles of classifiers by means of weighted instance selection. IEEE Transactions on Neural Networks 20 2 (2008) 258-277
-
(2008)
IEEE Transactions on Neural Networks
, vol.20
, Issue.2
, pp. 258-277
-
-
García-Pedrajas, N.1
|