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Peter Tino

School of Computer Science, University of Birmingham, UK;

Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices


Feb 01, 2021
Fengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji

* 15 pages, 7 figures 

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LAAT: Locally Aligned Ant Technique for detecting manifolds of varying density


Sep 17, 2020
Abolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tino

* Submitted to the IEEE for possible publication 

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Visualisation and knowledge discovery from interpretable models


May 08, 2020
Sreejita Ghosh, Peter Tino, Kerstin Bunte

* Accepted for proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020 

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Input representation in recurrent neural networks dynamics


Mar 24, 2020
Pietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tino


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Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information


Dec 10, 2019
Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer

* Preprint accepted at Neurocomputing 

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Dynamical Systems as Temporal Feature Spaces


Jul 15, 2019
Peter Tino

* 45 pages, 17 figures 

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Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets


Mar 24, 2019
Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk, Cesar Hervas-Martinez

* Published in the Thirty-Third AAAI Conference on Artificial Intelligence, 2019 

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A mixture of experts model for predicting persistent weather patterns


Mar 24, 2019
Maria Perez-Ortiz, Pedro A. Gutierrez, Peter Tino, Carlos Casanova-Mateo, Sancho Salcedo-Sanz

* Published in IEEE International Joint Conference on Neural Networks (IJCNN) 2018 

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Feature Relevance Bounds for Ordinal Regression


Feb 20, 2019
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer

* preprint of a paper accepted for oral presentation at the 27th European Symposium on Artificial Neural Networks (ESANN 2019) 

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Probabilistic Matching: Causal Inference under Measurement Errors


Mar 13, 2017
Fani Tsapeli, Peter Tino, Mirco Musolesi

* In Proceedings of International Joint Conference Of Neural Networks (IJCNN) 2017 

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A Classification Framework for Partially Observed Dynamical Systems


Jul 07, 2016
Yuan Shen, Peter Tino, Krasimira Tsaneva-Atanasova

* Phys. Rev. E 95, 043303 (2017) 

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Probabilistic classifiers with low rank indefinite kernels


Apr 08, 2016
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tino


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Autoencoding Time Series for Visualisation


May 05, 2015
Nikolaos Gianniotis, Dennis Kügler, Peter Tino, Kai Polsterer, Ranjeev Misra

* Published in ESANN 2015 

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Learning in the Model Space for Fault Diagnosis


Oct 31, 2012
Huanhuan Chen, Peter Tino, Xin Yao, Ali Rodan


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Scaling Up Estimation of Distribution Algorithms For Continuous Optimization


Nov 09, 2011
Weishan Dong, Tianshi Chen, Peter Tino, Xin Yao


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Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application


Aug 25, 2009
Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury, Xin Yao, Markus Harva

* 36 pages, 10 figures, 16 tables, accepted for publication in Pattern Recognition. This is a shortened version of the article: interested readers are urged to refer to the published version 

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How accurate are the time delay estimates in gravitational lensing?


May 01, 2006
Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury

* Astron.Astrophys. 454 (2006) 695-706 
* 14 pages, 12 figures; accepted for publication in Astronomy & Astrophysics 

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