39
Identification from Wearable Device Brain Signals
References
1. T. G. Dietterich, Approximate statistical tests for comparing supervised classification learning
algorithms, Neural Computation 10 (7) (1998), 1895–1923.
2. J. R. Quinlan, Induction of decision trees, Machine Learning 1 (1) (1986), 81–106.
3. C. Cortes and V. Vapnik, Support-vector networks, Machine Learning 20 (3) (1995), 273–297.
4. K. Hornik, M. Stinchcombe, H. White, Multilayer feed forward networks are universal approximators, Neural Networks 2 (5) (1989), 359–366.
5. V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, B. P. Feuston, Random forest:
A classification and regression tool for compound classification and QSAR modeling, Journal of
Chemical Information and Computer Sciences 43 (6) (2003), 1947–1958.
6. G. Peters, Evolutionary rough k-medoid clustering, Transactions on Rough Sets VIII (2008),
Springer, Berlin, pp. 289–306.
7. P. Lingras, Evolutionary rough k-means algorithm, Proceedings of Rough Set and Knowledge
Technologies 2009, Lecture Notes in Computer Science 5589, Springer Verlag (2009), Gold Coast,
QLD, Australia, pp. 68–75.
8. P. Lingras, Rough k-medoid clustering using gas, Proceedings of ICCI 2009, Hong Kong,
pp. 315–319.
9. W. Dement and N. Kleitman, Cyclic variations in EEG during sleep and their relation to eye
movements, body motility, and dreaming, Electroencephalography and Clinical Neurophysiology
9 (4) (1957), 673–690.
10. G. T. Carolin Strobl and J. Malley, An introduction to recursive partitioning: Rationale,
application, and characteristics of classification and regression trees, bagging, and random
forests, Psychological Methods 14 (4) (2009), 323–348.
11. A. J. Myles, et al., An introduction to decision tree modeling, Journal of Chemometrics 18 (6),
275–285.
12. E. Fernandez-Blanco, et al., Random forest classification based on star graph topological indices
for antioxidant proteins, Journal of Theoretical Biology 317 (2013), 331–337.
13. Z.-X. Yang, et al., Multiple birth support vector machine for multi-class classification, Neural
Computing & Applications 22 (1) (2013), S153–S161.
14. P. L. Zhibin Li, et al., Using support vector machine models for crash injury severity analysis,
Accident Analysis and Prevention 45 (2012), 478–486.
15. M. Sun, A multi-class support vector machine: Theory and model, International Journal of
Information Technology & Decision Making 12 (6) (2013), 1175–1199.
16. A. Zeileis. K. Hornik, A. Smola and A. Karatzoglou, kernlab-an S4 package for kernel methods
in R, Journal of statistical software 11 (9) (2004), 1–20.
17. C. M. Bishop, Neural networks and their applications, Review of Scientific Instruments 65 (6)
(1994) 1803–1832.
18. M. Gevrey, et al., Review and comparison of methods to study the contribution of variables in
artificial neural network models, Ecological Modelling 160 (2003), 249–264.
19. R. A. McIndoe, et al., Parakmeans: Implementation of a parallelized k-means algorithm suitable
for general laboratory use, BMC Bioinformatics 9 (200) (2008).
20. Z. Pawlak, Rough sets, International Journal of Information and Computer Sciences 11 (1982),
145–172.
21. Z. Pawlak, Rough sets: Theoretical aspects of reasoning about data, Kluwer Academic. Boston, MA.
1991.
22. Y. Yao, Constructive and algebraic methods of the theory of rough sets, Information Sciences 109
(1998), 21–47.
23. A. S. L. Polkowski, Rough mereology: A new paradigm for approximate reasoning, International
Journal of Approximate Reasoning 15 (4) (1996), 333–365.
Identification from Wearable Device Brain Signals
References
1. T. G. Dietterich, Approximate statistical tests for comparing supervised classification learning
algorithms, Neural Computation 10 (7) (1998), 1895–1923.
2. J. R. Quinlan, Induction of decision trees, Machine Learning 1 (1) (1986), 81–106.
3. C. Cortes and V. Vapnik, Support-vector networks, Machine Learning 20 (3) (1995), 273–297.
4. K. Hornik, M. Stinchcombe, H. White, Multilayer feed forward networks are universal approximators, Neural Networks 2 (5) (1989), 359–366.
5. V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, B. P. Feuston, Random forest:
A classification and regression tool for compound classification and QSAR modeling, Journal of
Chemical Information and Computer Sciences 43 (6) (2003), 1947–1958.
6. G. Peters, Evolutionary rough k-medoid clustering, Transactions on Rough Sets VIII (2008),
Springer, Berlin, pp. 289–306.
7. P. Lingras, Evolutionary rough k-means algorithm, Proceedings of Rough Set and Knowledge
Technologies 2009, Lecture Notes in Computer Science 5589, Springer Verlag (2009), Gold Coast,
QLD, Australia, pp. 68–75.
8. P. Lingras, Rough k-medoid clustering using gas, Proceedings of ICCI 2009, Hong Kong,
pp. 315–319.
9. W. Dement and N. Kleitman, Cyclic variations in EEG during sleep and their relation to eye
movements, body motility, and dreaming, Electroencephalography and Clinical Neurophysiology
9 (4) (1957), 673–690.
10. G. T. Carolin Strobl and J. Malley, An introduction to recursive partitioning: Rationale,
application, and characteristics of classification and regression trees, bagging, and random
forests, Psychological Methods 14 (4) (2009), 323–348.
11. A. J. Myles, et al., An introduction to decision tree modeling, Journal of Chemometrics 18 (6),
275–285.
12. E. Fernandez-Blanco, et al., Random forest classification based on star graph topological indices
for antioxidant proteins, Journal of Theoretical Biology 317 (2013), 331–337.
13. Z.-X. Yang, et al., Multiple birth support vector machine for multi-class classification, Neural
Computing & Applications 22 (1) (2013), S153–S161.
14. P. L. Zhibin Li, et al., Using support vector machine models for crash injury severity analysis,
Accident Analysis and Prevention 45 (2012), 478–486.
15. M. Sun, A multi-class support vector machine: Theory and model, International Journal of
Information Technology & Decision Making 12 (6) (2013), 1175–1199.
16. A. Zeileis. K. Hornik, A. Smola and A. Karatzoglou, kernlab-an S4 package for kernel methods
in R, Journal of statistical software 11 (9) (2004), 1–20.
17. C. M. Bishop, Neural networks and their applications, Review of Scientific Instruments 65 (6)
(1994) 1803–1832.
18. M. Gevrey, et al., Review and comparison of methods to study the contribution of variables in
artificial neural network models, Ecological Modelling 160 (2003), 249–264.
19. R. A. McIndoe, et al., Parakmeans: Implementation of a parallelized k-means algorithm suitable
for general laboratory use, BMC Bioinformatics 9 (200) (2008).
20. Z. Pawlak, Rough sets, International Journal of Information and Computer Sciences 11 (1982),
145–172.
21. Z. Pawlak, Rough sets: Theoretical aspects of reasoning about data, Kluwer Academic. Boston, MA.
1991.
22. Y. Yao, Constructive and algebraic methods of the theory of rough sets, Information Sciences 109
(1998), 21–47.
23. A. S. L. Polkowski, Rough mereology: A new paradigm for approximate reasoning, International
Journal of Approximate Reasoning 15 (4) (1996), 333–365.