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1
SMOTE: Synthetic Minority Over-sampling Technique
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SMOTE: Synthetic Minority Over-sampling Technique

The Journal of artificial intelligence research, 2002-01, Vol.16, p.321-357 [Peer Reviewed Journal]

2002. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.953

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2
Decision templates for multiple classifier fusion: an experimental comparison
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Decision templates for multiple classifier fusion: an experimental comparison

Pattern recognition, 2001-02, Vol.34 (2), p.299-314 [Peer Reviewed Journal]

2001 Pattern Recognition Society ;ISSN: 0031-3203 ;EISSN: 1873-5142 ;DOI: 10.1016/S0031-3203(99)00223-X

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3
Learning classifier systems resources
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Learning classifier systems resources

Soft computing (Berlin, Germany), 2002-06, Vol.6 (3-4), p.240-243 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2002. ;ISSN: 1432-7643 ;EISSN: 1433-7479 ;DOI: 10.1007/s005000100119

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4
Combining multiple classifiers by averaging or by multiplying?
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Combining multiple classifiers by averaging or by multiplying?

Pattern recognition, 2000-09, Vol.33 (9), p.1475-1485 [Peer Reviewed Journal]

2000 Pattern Recognition Society ;ISSN: 0031-3203 ;EISSN: 1873-5142 ;DOI: 10.1016/S0031-3203(99)00138-7

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5
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
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Bagging, Boosting and the Random Subspace Method for Linear Classifiers

Pattern analysis and applications : PAA, , Vol.5 (2), p.121-135 [Peer Reviewed Journal]

ISSN: 1433-7541 ;EISSN: 1433-755X ;DOI: 10.1007/s100440200011

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6
On combining classifiers using sum and product rules
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On combining classifiers using sum and product rules

Pattern recognition letters, 2001-10, Vol.22 (12), p.1283-1289 [Peer Reviewed Journal]

2001 Elsevier Science B.V. ;ISSN: 0167-8655 ;EISSN: 1872-7344 ;DOI: 10.1016/S0167-8655(01)00073-3

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7
A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence
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A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence

The Journal of artificial intelligence research, 2002-01, Vol.17, p.333-361 [Peer Reviewed Journal]

2002. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1026

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8
A new signature verification technique based on a two-stage neural network classifier
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A new signature verification technique based on a two-stage neural network classifier

Engineering applications of artificial intelligence, 2001-02, Vol.14 (1), p.95-103 [Peer Reviewed Journal]

2001 Elsevier Science Ltd ;ISSN: 0952-1976 ;EISSN: 1873-6769 ;DOI: 10.1016/S0952-1976(00)00064-6

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9
Hierarchical text categorization using neural networks
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Hierarchical text categorization using neural networks

Information retrieval (Boston), 2002, Vol.5 (1), p.87-118 [Peer Reviewed Journal]

2002 INIST-CNRS ;Copyright (c) 2002 Kluwer Academic Publishers ;Kluwer Academic Publishers 2002. ;ISSN: 1386-4564 ;EISSN: 1573-7659 ;DOI: 10.1023/A:1012782908347

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10
Design of effective neural network ensembles for image classification purposes
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Design of effective neural network ensembles for image classification purposes

Image and vision computing, 2001-08, Vol.19 (9), p.699-707 [Peer Reviewed Journal]

2001 Elsevier Science B.V. ;ISSN: 0262-8856 ;EISSN: 1872-8138 ;DOI: 10.1016/S0262-8856(01)00045-2

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11
The Relaxed Online Maximum Margin Algorithm
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The Relaxed Online Maximum Margin Algorithm

Machine learning, 2002-01, Vol.46 (1-3), p.361-387 [Peer Reviewed Journal]

Kluwer Academic Publishers 2002 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1023/A:1012435301888

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12
Robust Learning with Missing Data
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Robust Learning with Missing Data

Machine learning, 2001-11, Vol.45 (2), p.147 [Peer Reviewed Journal]

Kluwer Academic Publishers 2001 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1023/A:1010968702992

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13
YACS: a new learning classifier system using anticipation
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YACS: a new learning classifier system using anticipation

Soft computing (Berlin, Germany), 2002-06, Vol.6 (3-4), p.216-228 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2002. ;ISSN: 1432-7643 ;EISSN: 1433-7479 ;DOI: 10.1007/s005000100117

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14
Combination of neural and statistical algorithms for supervised classification of remote-sensing images
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Combination of neural and statistical algorithms for supervised classification of remote-sensing images

Pattern recognition letters, 2000-05, Vol.21 (5), p.385-397 [Peer Reviewed Journal]

2000 Elsevier Science B.V. ;ISSN: 0167-8655 ;EISSN: 1872-7344 ;DOI: 10.1016/S0167-8655(00)00006-4

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15
Performance evaluation of pattern classifiers for handwritten character recognition
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Performance evaluation of pattern classifiers for handwritten character recognition

International journal on document analysis and recognition, 2002-03, Vol.4 (3), p.191-204 [Peer Reviewed Journal]

ISSN: 1433-2833 ;EISSN: 1433-2825 ;DOI: 10.1007/s100320200076

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16
Binary labelling and decision-level fusion
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Binary labelling and decision-level fusion

Information fusion, 2001, Vol.2 (2), p.103-112 [Peer Reviewed Journal]

2001 Elsevier Science B.V. ;ISSN: 1566-2535 ;EISSN: 1872-6305 ;DOI: 10.1016/S1566-2535(01)00029-X

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17
Comparison of methodologies for probabilistic quantitative precipitation forecasting
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Comparison of methodologies for probabilistic quantitative precipitation forecasting

Weather and forecasting, 2002-08, Vol.17 (4), p.783-799 [Peer Reviewed Journal]

2002 INIST-CNRS ;Copyright American Meteorological Society 2002 ;ISSN: 0882-8156 ;EISSN: 1520-0434 ;DOI: 10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2 ;CODEN: WEFOE3

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18
Adaptive soft k-nearest-neighbour classifiers
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Adaptive soft k-nearest-neighbour classifiers

Pattern recognition, 2000-12, Vol.33 (12), p.1999-2005 [Peer Reviewed Journal]

2000 Pattern Recognition Society ;ISSN: 0031-3203 ;EISSN: 1873-5142 ;DOI: 10.1016/S0031-3203(99)00186-7

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19
What should a classifier system learn and how should we measure it?
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What should a classifier system learn and how should we measure it?

Soft computing (Berlin, Germany), 2002-06, Vol.6 (3-4), p.171-182 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2002. ;ISSN: 1432-7643 ;EISSN: 1433-7479 ;DOI: 10.1007/s005000100114

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20
The stability of long action chains in XCS
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The stability of long action chains in XCS

Soft computing (Berlin, Germany), 2002-06, Vol.6 (3-4), p.183-199 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2002. ;ISSN: 1432-7643 ;EISSN: 1433-7479 ;DOI: 10.1007/s005000100115

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