Pattern recognition in collective cognitive systems: Hybrid human-machine learning (HHML) by heterogeneous ensembles

Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI 2010

Volumn 1, Issue , 2010, Pages 183-188

  Dashti, Hesam T ^a   Ardalan, Adel ^b   Siahpirani, Alireza F ^b   Tonejc, Jernej ^a   Uilecan, Ioan V ^a   Simas, Tiago ^c   Miranda, Bruno ^c   Ribeiro, Rita ^c   Wang, Liya ^d   Assadi, Amir H ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b UNIVERSITY OF TEHRAN   (Iran)

^c UNINOVA   (Portugal)

^d Lustgarten Pancreatic Cancer Research Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION DOMAINS; BROAD SPECTRUM; BUILDING BLOCKES; COMPLEX TASK; DATA SETS; DIGITAL COMMUNICATIONS; FUNCTIONAL COMPONENTS; HUMAN EXPERT; HUMAN-MACHINE; HYBRID ARCHITECTURES; HYBRID NETWORK; PRIOR KNOWLEDGE; PSEUDO-LAYERS; TEAM COLLABORATION; THEORETICAL FOUNDATIONS; VISUAL PERCEPTION;

ABSTRACTING; ARTIFICIAL INTELLIGENCE; ASTRONOMY; DIGITAL COMMUNICATION SYSTEMS; DYNAMICAL SYSTEMS; FEATURE EXTRACTION; INTELLIGENT AGENTS; LEARNING SYSTEMS; PLANTS (BOTANY); VISION;

COGNITIVE SYSTEMS;

EID: 84866075596     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. V. N. Vapnik The Nature of Statistical Learning Theory 1998.
2. V. N. Vapnik Statistical Learning Theory 1998.
3. E. Schneidman, et al. Network information and connected correlations Phys Rev Lett, vol. 91, 2003.
4. L. C. Osborne, et al. Time course of information about motion direction in visual area MT of macaque monkeys, J Neurosci, vol. 24, 2004.
5. J. Bouvrie, et al. On Invariance in Hierarchical Models. Advances in Neural Information Processing Systems, vol. 22, 2009.
6. S. Chikkerur, et al., A Bayesian inference theory of attention: neuroscience and algorithms M1T-CSAIL-TR-, 2009.
7. Tomaso Poggio and S. Smale The Mathematics of Learning: Dealing with data. Notices of the AMS, vol. 50, 2003.
8. J.-M. Fellous, et al. Discovering spike patterns in neuronal responses Journal of Neuroscience, 2004.
9. S. B. Laughlin and T. J. Sejnowski Communication in neuronal networks Science, vol. 301, 2003.
10. A. P. Prescott, et al. Interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons Neuroscience, vol. 26, 2006.
11. B. A. Olshausen and D. J. Field Vision and the Coding of Natural Images American Scientist, vol. 88, pp. 238-24, 2000.
12. B. A. Olshausen and D. J. Field Sparse Coding of Sensory Inputs," Current Opinion in Neurobiologyt, vol. 14, pp. 481-487, 2004.
13. G. P. Zhang Neural Networks for Classification: A Survey IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, vol. 30, 2000.
14. M. J. Healy and T. P. Caudell A Categorical Semantic Analysis of ART Architectures presented at the IJCNN'01: International Joint Conference on Neural Networks, 2001.
15. L. K. Stefano Rubele, Leo Girardi The star formation history of the SMC star cluster NGC419 Solar and Stellar Astrophysics, 2009.
16. OGLE Available: http://ogle.astrouw.edu.pl/
17. J. D. L. M. Sarro, C. Aerts, M. Löpez Comparative clustering analysis of variable stars in the Hipparcos, OGLE Large Magellanic Cloud and CoRoT exoplanet databases Astronomy and Astrophysics, 2009
18. M. V. Rockman Reverse engineering the genotypeDphenotype map with natural genetic variation Nature, 2008
19. GAIA Available: http://sci.esa.int/science-e/www/area/index.cfm?fareaid= 26
20. ESA Available: http://www.esa.int/esaCP/index.html
21. H. B. BARLOW Pattern recognition and the responses of sensory neurons Annals of the New York Academy of Sciences, vol. 156, pp. 872-881, 1969.
22. H. B. BARLOW Cerebral cortex as a model builder John Wiley, 1985.
23. H. B. BARLOW Matters of Intelligence John Wiley, 1987
24. M. DeWeese and W. Bialek Information flow in sensory neuronsll Nuovo Cimento D, vol. 17, pp. 733-741, 1995.
25. D. Warland, et al. Reading between the spikes in the cricket cereal afferent system Analysis and Modeling of Neural Systems, pp. 327-333, 1991.
26. T. Poggio A theory of how the brain might work presented at the In Cold Spring Harbor Symposia on Quantitative Biology, 1990.
27. A. J. Bell and T. J. Sejnowski An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation Neural Computation, vol. 7, pp. 1129-1159, 1995.