P. Slowinski, F. Alderisio, C. Zhai, Y. Shen, P. Tino, C. Bortolon, D. Capdevielle, L. Cohen, M. Khoramshahi, A. Billard, R. Salesse, M. Gueugnon, L. Marin, B.G. Bardy, M. di Bernardo, S. Raffard, K. Tsaneva-Atanasova:
Unravelling socio-motor biomarkers in schizophrenia.
npj Schizophrenia,
1(3), DOI:10.1038/s41537-016-0009-x, 2017.
X. Yao, E. Burke, J.A. Lozano, J. Smith,J.J. Merelo-Guervos,
J.A. Bullinaria, J. Rowe, P. Tino, A. Kaban, H.P. Schwefel (Eds.):
Parallel Problem Solving from Nature - PPSN VIII .
Springer, Lecture Notes in Computer Science, Vol. 3242, 2004.
M. Canducci, A. Taghribi, M. Mastropietro, S. de Rijcke, R. Peletier, K. Bunte, P. Tino:
Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations.
22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2021),
pp. 493--501, Lecture Notes in Computer Science, Springer International Publishing, LNCS 13113,
2021.
X. Chen, Y. Shen, E. Zavala, K. Tsaneva-Atanasova, T. Upton, G. Russell, P. Tino:
SOMiMS - Topographic Mapping in the Model Space.
22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2021),
pp. 502-510, Lecture Notes in Computer Science, Springer International Publishing, LNCS 13113,
2021.
G. Serra, Z. Xu, M. Niepert, C. Lawrence, P. Tino, X. Yao:
Interpreting Node Embedding with Text-labeled Graphs.
In
International Joint Conference on Neural Networks - IJCNN 2021,
doi: 10.1109/IJCNN52387.2021.9533692, 2021.
A. Taghribi, K. Bunte, M. Mastropietro, S. D. Rijcke, P. Tino:
ASAP - A Sub-sampling Approach for Preserving Topological Structures.
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020).
D-facto Publications, ISBN 978-2-87587-074-2, 2020.
P. Tino:
Fisher Memory of Linear Wigner Echo State Networks.
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017).
pp. 87-92, D-facto Publications, 2017.
S. Al Otaibi, P. Tino, S. Raychaudhury:
Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams.
17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2016),
pp. 552-559, Lecture Notes in Computer Science, Springer-Verlag, LNCS 9937,
2016.
Best Conference Paper Award (Applications track)
R.T. Ibrahem, P. Tino, R.J. Pearson, T.J. Ponman, A. Babul:
Automated Detection of Galaxy Groups Through Probabilistic Hough Transform.
Neural Information Processing - Proceedings of the 22nd International Conference on Neural Information Processing (ICONIP 2015).
pp. 323-331, Lecture Notes in Computer Science 9491, Springer-Verlag, 2015.
N. Gianniotis, S.D. Kuegler, P. Tino, K. Polsterer, R. Misra:
Autoencoding time series for visualisation.
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015).
pp. 495-500, 2015.
F.M. Schleif, A. Gisbrecht, P. Tino:
Large scale Indefinite Kernel Fisher Discriminant.
3rd International Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD 2015).
pp. 160-170, Lecture Notes in Computer Science 9370, Springer-Verlag, 2015.
A. Elhabbash, R. Bahsoon, P. Tino, P.R. Lewis:
A Utility Model for Volunteered Service Composition.
Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2014).
pp. 337-344,
IEEE Computer Society, 2014.
A. Elhabbash, R. Bahsoon, P. Tino:
Towards Self-aware Service Composition.
16th IEEE International Conference on High Performance Computing and Communications.
pp. 1275-1279,
IEEE Press, 2014.
Sh. Fouad, P. Tino,:
Prototype Based Modelling for Ordinal Classification.
13th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2012),
pp. 208-215, Lecture Notes in Computer Science, Springer-Verlag, LNCS 7435,
2012.
J. Mazgut, M. Pulinyova, P. Tino:
Using dimensionality reduction method for binary data to questionnaire analysis.
In
Proceedings of the 7th international conference on Mathematical and Engineering Methods in Computer Science (MEMICS 2011),
pp. 146-154,
Lecture Notes in Computer Science, Springer-Verlag, LNCS 7119,
2012.
Ph. Weber, B. Bordbar, P. Tino:
A Principled Approach to the Analysis of Process Mining Algorithms.
12th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2011),
pp. 474-481, Lecture Notes in Computer Science, Springer-Verlag, LNCS 6936,
2011.
X. Wang, P. Tino, M. Fardal, S. Raychaudhury, A. Babul:
Fast Parzen Window Density Estimator.
In
International Joint Conference on Neural Networks - IJCNN 2009,
pp. 3267-3274,
IEEE Computer Society, 2009.
X. Wang, P. Tino, M. Fardal:
Multiple Manifold Learning Framework based on Hierarchical Mixture Density Model.
In
Machine Learning and Knowledge Discovery in Databases (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2008),
pp. 566-581,
Lecture Notes in Computer Science, LNCS 4984, Springer-Verlag, 2008.
M. Cernansky, P. Tino:
Predictive Modeling with Echo State Networks
.
In
18th International Conference on Artificial Neural Networks - ICANN 2008,
(eds) V. Kurkova, R. Neruda, J. Koutnik.
pp. 778-787,
Lecture Notes in Computer Science, LNCS 5163, Springer-Verlag, 2008.
P. Tino:
On Conditions for Intermittent Search in Self-organizing
Neural Networks.
In
Advances in Artificial Intelligence - 6th Mexican International Conference on Artificial Intelligence - MICAI 2007,
(eds) A. Gelbukh, A. Fernando, K. Morales.
pp. 172-181,
Lecture Notes in Computer Science (4827),Springer-Verlag, 2007.
H. Chen, P. Tino, X. Yao:
A Probabilistic Ensemble Pruning Algorithm.
In
6th IEEE International Conference on Data Mining - ICDM06 - Workshops,
(eds) .
pp. 878-882,
IEEE Computer Society, 2006.
J.M. Binner, B. Jones, G. Kendal, J. Tepper, P. Tino:
Does Money Matter?
An Artificial Intelligence Approach.
.
In
Proceedings of 9th Joint Conference on
Information Sciences 2006 (5th International
Conference on Computational Intelligence in Economics and Finance),
Kaohsiung, Taiwan. pp 72-75, 2006.
P. Tino, N. Nikolaev, X. Yao :
Volatility Forecasting with Sparse Bayesian Kernel Models.
In
Proceedings of 8th Joint Conference on
Information Sciences 2005 (4th International
Conference on Computational Intelligence in Economics and Finance),
Salt Lake City, UT. pp 1150-1153, 2005.
P. Tino, B. Hammer:
Architectural Bias in Recurrent Neural Networks - Fractal Analysis.
In
Artificial Neural Networks - ICANN 2002,
(ed.) J.R.Dorronsoro. pp. 1359-1364,
Lecture Notes in Computer Science, Springer-Verlag, 2002.
Best Conference Paper Award (European Neural Network Society, 2002)
P. Tino, M. Cernansky, L. Benuskova:
Markovian Architectural Bias of Recurrent Neural Networks .
In
Intelligent Technologies - Theory and Applications.
Frontiers in AI and Applications
, vol. 76,
(eds) P. Sincak, J. Vascak, V. Kvasnicka and J. Pospichal.
pp. 17-23,
IOS Press, Amsterdam, 2002.
P. Tino, I. Nabney, Yi Sun, B.S. Williams:
A Principled Approach to Interactive Hierarchical Non-Linear Visualization
of High-Dimensional Data.
In
Computing Science and Statistics,
Volume 33: Frontiers in Data Mining and Bioinformatics.
Proceedings of the 33rd Symposium on the Interface.
(eds) E.J. Wegman,
A. Braverman,
A. Goodman, P. Smyth.
pp. 580-587,
Interface Foundation of North America,
2002.
Sh. Parfitt, P. Tino, G. Dorffner:
Graded grammaticality in Prediction Fractal Machines.
In
Advances in Neural Information Processing Systems 12,
(eds) S. A. Solla, T. K. Leen, K-R. Müller.
pp. 52-58, MIT Press, 2000.
P. Tino, Ch. Schittenkopf, G. Dorffner, E.J. Dockner:
A Symbolic Dynamics Approach to Volatility Prediction.
In
Computational Finance,
(eds) Y.S. Abu-Mostafa, B. LeBaron, A.W. Lo, A.S. Weigend.
pp. 137-151, MIT Press, Cambridge, MA, 2000.
T. Lin, B.G. Horne, P. Tino, C.L. Giles:
Long-term dependencies in NARX networks.
In
Proceedings of the World Congress on Neural Networks,
Washington D.C.,
Vol 3, pp. 142-146, 1995.
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