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Peter Tino
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- affiliation: University of Birmingham, School of Computer Science
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2020 – today
- 2024
[j79]Beata Ondrusova, Peter Tino
, Jana Svehlíková:
Optimal electrode placements for localizing premature ventricular contractions using a single dipole cardiac source model. Comput. Biol. Medicine 183: 109264 (2024)- [j78]Boyu Li, Robert Simon Fong, Peter Tino:
Simple Cycle Reservoirs are Universal. J. Mach. Learn. Res. 25: 158:1-158:28 (2024) - [c109]Giuseppe Serra, Peter Tino, Zhao Xu, Xin Yao:
An Interpretable Alternative to Neural Representation Learning for Rating Prediction - Transparent Latent Class Modeling of User Reviews. IJCNN 2024: 1-8 - [c108]Peter Tino, Robert Simon Fong, Roberto Fabio Leonarduzzi:
Predictive Modeling in the Reservoir Kernel Motif Space. IJCNN 2024: 1-8 - [i29]Peter Tino, Robert Simon Fong, Roberto Fabio Leonarduzzi:
Predictive Modeling in the Reservoir Kernel Motif Space. CoRR abs/2405.07045 (2024) - [i28]Giuseppe Serra, Peter Tino, Zhao Xu, Xin Yao:
An Interpretable Alternative to Neural Representation Learning for Rating Prediction - Transparent Latent Class Modeling of User Reviews. CoRR abs/2407.00063 (2024) - [i27]Robert Simon Fong, Boyu Li, Peter Tino:
Universality of Real Minimal Complexity Reservoir. CoRR abs/2408.08071 (2024) - [i26]Antony Lee, Peter Tino, Iain Bruce Styles:
A distance function for stochastic matrices. CoRR abs/2410.12689 (2024) - 2023
- [j77]Fengzhen Tang, Peter Tino, Haibin Yu:
Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold. IEEE Trans. Cybern. 53(8): 5178-5190 (2023) - [j76]Shuyi Zhang, Peter Tino, Xin Yao:
Hierarchical Reduced-Space Drift Detection Framework for Multivariate Supervised Data Streams. IEEE Trans. Knowl. Data Eng. 35(3): 2628-2640 (2023) - [j75]Abolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tino:
LAAT: Locally Aligned Ant Technique for Discovering Multiple Faint Low Dimensional Structures of Varying Density. IEEE Trans. Knowl. Data Eng. 35(6): 6014-6027 (2023) - [c107]Hayatullahi Bolaji Adeyemo, Rami Bahsoon, Peter Tino:
An Approach for Dynamic Behavioural Prediction and Fault Injection in Cyber-Physical Systems. BDCAT 2023: 08:1-08:6 - [c106]Beata Ondrusova, Peter Tino, Jana Svehlíková:
Inverse Solution Accuracy Using 12-Lead ECG vs 9 Significant Electrodes Derived by Greedy Algorithm. CinC 2023: 1-4 - [c105]Vahab Samandi, Peter Tino, Rami Bahsoon:
Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List Scheduling. HAIS 2023: 244-255 - [i25]Boyu Li, Robert Simon Fong, Peter Tino:
Simple Cycle Reservoirs are Universal. CoRR abs/2308.10793 (2023) - 2022
- [b1]Robert Simon Fong, Peter Tino:
Population-Based Optimization on Riemannian Manifolds. Studies in Computational Intelligence 1046, Springer 2022, ISBN 978-3-031-04292-8, pp. 1-165 - [j74]Marco Canducci, Peter Tiño, Michele Mastropietro:
Probabilistic modelling of general noisy multi-manifold data sets. Artif. Intell. 302: 103579 (2022) - [j73]Marco Canducci, P. Awad, Abolfazl Taghribi, Mohammad Mohammadi, Michele Mastropietro, Sven De Rijcke, Reynier Peletier, Rory Smith, Kerstin Bunte, Peter Tiño:
1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments. Astron. Comput. 41: 100658 (2022) - [j72]Abolfazl Taghribi, Marco Canducci, Michele Mastropietro, Sven De Rijcke, Kerstin Bunte, Peter Tino:
ASAP - A sub-sampling approach for preserving topological structures modeled with geodesic topographic mapping. Neurocomputing 470: 376-388 (2022) - [j71]Mohammad Mohammadi, Peter Tino, Kerstin Bunte:
Manifold Alignment Aware Ants: A Markovian Process for Manifold Extraction. Neural Comput. 34(3): 595-641 (2022) - [j70]Pietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tino:
Input-to-State Representation in Linear Reservoirs Dynamics. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4598-4609 (2022) - [c104]Hayatullahi Bolaji Adeyemo, Rami Bahsoon, Peter Tiño:
Surrogate-based Digital Twin for Predictive Fault Modelling and Testing of Cyber Physical Systems. BDCAT 2022: 166-169 - [c103]Beata Ondrusova, Jana Svehlíková, Milan Tysler, Peter Tino:
Greedy Selection of the Torso Electrodes for the Solution of Inverse Problem with a Single Dipole. CinC 2022: 1-4 - [c102]Vahab Samandi, Peter Tino, Rami Bahsoon:
Duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network. IDEAL 2022: 170-178 - [c101]Stephen Friess, Peter Tino, Stefan Menzel, Zhao Xu, Bernhard Sendhoff, Xin Yao:
Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction. IJCNN 2022: 1-8 - [e7]Hujun Yin, David Camacho, Peter Tiño:
Intelligent Data Engineering and Automated Learning - IDEAL 2022 - 23rd International Conference, IDEAL 2022, Manchester, UK, November 24-26, 2022, Proceedings. Lecture Notes in Computer Science 13756, Springer 2022, ISBN 978-3-031-21752-4 [contents] - [i24]Sreejita Ghosh, Elizabeth Sarah Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte:
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets. CoRR abs/2206.02056 (2022) - 2021
- [j69]Seyma Kucukozer Cavdar, Tugba Taskaya-Temizel, Abhinav Mehrotra, Mirco Musolesi, Peter Tino:
Designing Robust Models for Behaviour Prediction Using Sparse Data from Mobile Sensing: A Case Study of Office Workers' Availability for Well-being Interventions. ACM Trans. Comput. Heal. 2(4): 29:1-29:33 (2021) - [j68]Fengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji:
Probabilistic learning vector quantization on manifold of symmetric positive definite matrices. Neural Networks 142: 105-118 (2021) - [j67]Yu Zhang, Peter Tiño, Ales Leonardis, Ke Tang:
A Survey on Neural Network Interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5(5): 726-742 (2021) - [j66]Fengzhen Tang, Mengling Fan, Peter Tiño:
Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices. IEEE Trans. Neural Networks Learn. Syst. 32(1): 281-292 (2021) - [c100]Beata Ondrusova, Jana Svehlíková, Jan Zelinka, Milan Tysler, Peter Tino:
Model-Based Relevance of Measuring Electrodes for the Inverse Solution with a Single Dipole. CinC 2021: 1-4 - [c99]Abdessalam Elhabbash, Rami Bahsoon, Peter Tino, Peter R. Lewis, Yehia Elkhatib:
Attaining Meta-self-awareness through Assessment of Quality-of-Knowledge. ICWS 2021: 712-723 - [c98]Marco Canducci, Abolfazl Taghribi, Michele Mastropietro, Sven De Rijcke, Reynier Peletier, Kerstin Bunte, Peter Tino:
Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations. IDEAL 2021: 493-501 - [c97]Xinyue Chen, Yuan Shen, Eder Zavala, Krasimira Tsaneva-Atanasova, Thomas Upton, Georgina Russell, Peter Tino:
SOMiMS - Topographic Mapping in the Model Space. IDEAL 2021: 502-510 - [c96]Stephen Friess, Peter Tiño, Zhao Xu, Stefan Menzel, Bernhard Sendhoff, Xin Yao:
Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization. IJCNN 2021: 1-9 - [c95]Giuseppe Serra, Zhao Xu, Mathias Niepert, Carolin Lawrence, Peter Tiño, Xin Yao:
Interpreting Node Embedding with Text-labeled Graphs. IJCNN 2021: 1-8 - [c94]Shuyi Zhang, Chao Pan, Liyan Song, Xiaoyu Wu, Zheng Hu, Ke Pei, Peter Tino, Xin Yao:
Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection. ECML/PKDD (3) 2021: 795-810 - [c93]Stephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao:
Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems. SSCI 2021: 1-7 - [e6]Hujun Yin, David Camacho, Peter Tiño, Richard Allmendinger, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento:
Intelligent Data Engineering and Automated Learning - IDEAL 2021 - 22nd International Conference, IDEAL 2021, Manchester, UK, November 25-27, 2021, Proceedings. Lecture Notes in Computer Science 13113, Springer 2021, ISBN 978-3-030-91607-7 [contents] - [i23]Fengzhen Tang, Haifeng Feng, Peter Tiño, Bailu Si, Daxiong Ji:
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices. CoRR abs/2102.00667 (2021) - 2020
- [j65]Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer:
Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. Neurocomputing 416: 266-279 (2020) - [j64]Seyma Kucukozer Cavdar, Tugba Taskaya-Temizel, Mirco Musolesi, Peter Tiño:
A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4(1): 15:1-15:38 (2020) - [j63]Peter Tiño:
Dynamical Systems as Temporal Feature Spaces. J. Mach. Learn. Res. 21: 44:1-44:42 (2020) - [j62]Frank-Michael Schleif, Christoph Raab, Peter Tiño:
Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. 129: 1-7 (2020) - [c92]Stephen Friess, Peter Tiño, Stefan Menzel, Bernhard Sendhoff, Xin Yao:
Representing Experience in Continuous Evolutionary optimisation through Problem-tailored Search Operators. CEC 2020: 1-7 - [c91]Abolfazl Taghribi, Kerstin Bunte, Michele Mastropietro, Sven De Rijcke, Peter Tiño:
ASAP - A Sub-sampling Approach for Preserving Topological Structures. ESANN 2020: 67-72 - [c90]Sreejita Ghosh, Peter Tiño, Kerstin Bunte:
Visualisation and knowledge discovery from interpretable models. IJCNN 2020: 1-8 - [c89]Stephen Friess, Peter Tiño, Stefan Menzel, Bernhard Sendhoff, Xin Yao:
Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances. PPSN (1) 2020: 583-596 - [i22]Pietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tiño:
Input representation in recurrent neural networks dynamics. CoRR abs/2003.10585 (2020) - [i21]Sreejita Ghosh, Peter Tiño, Kerstin Bunte:
Visualisation and knowledge discovery from interpretable models. CoRR abs/2005.03632 (2020) - [i20]Abolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tiño:
LAAT: Locally Aligned Ant Technique for detecting manifolds of varying density. CoRR abs/2009.08326 (2020) - [i19]Tom Goodman, Karoline van Gemst, Peter Tiño:
A Geometric Framework for Pitch Estimation on Acoustic Musical Signals. CoRR abs/2012.04517 (2020) - [i18]Yu Zhang, Peter Tiño, Ales Leonardis, Ke Tang:
A Survey on Neural Network Interpretability. CoRR abs/2012.14261 (2020)
2010 – 2019
- 2019
- [j61]Siang Yew Chong, Peter Tiño, Jun He:
Coevolutionary systems and PageRank. Artif. Intell. 277 (2019) - [j60]Siang Yew Chong, Peter Tiño, Jun He, Xin Yao:
A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks. Evol. Comput. 27(2): 195-228 (2019) - [j59]Abdessalam Elhabbash, Maria Salama, Rami Bahsoon, Peter Tiño:
Self-awareness in Software Engineering: A Systematic Literature Review. ACM Trans. Auton. Adapt. Syst. 14(2): 5:1-5:42 (2019) - [c88]María Pérez-Ortiz, Peter Tiño, Rafal Mantiuk, César Hervás-Martínez:
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets. AAAI 2019: 4715-4722 - [c87]Jana Svehlíková, Jan Zelinka, Milan Tysler, Peter Tiño:
Multiobjective Optimization Approach to Localization of Ectopic Beats by Single Dipole: Case Study. CinC 2019: 1-4 - [c86]Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer:
Feature relevance bounds for ordinal regression. ESANN 2019 - [c85]Robert Simon Fong, Peter Tiño:
Extended stochastic derivative-free optimization on riemannian manifolds. GECCO (Companion) 2019: 257-258 - [c84]Stephen Friess, Peter Tiño, Stefan Menzel, Bernhard Sendhoff, Xin Yao:
Learning Transferable Variation Operators in a Continuous Genetic Algorithm. SSCI 2019: 2027-2033 - [e5]Hujun Yin, David Camacho, Peter Tiño, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, Richard Allmendinger:
Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11871, Springer 2019, ISBN 978-3-030-33606-6 [contents] - [e4]Hujun Yin, David Camacho, Peter Tiño, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, Richard Allmendinger:
Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11872, Springer 2019, ISBN 978-3-030-33616-5 [contents] - [i17]Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer:
Feature Relevance Bounds for Ordinal Regression. CoRR abs/1902.07662 (2019) - [i16]María Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, Carlos Casanova-Mateo, Sancho Salcedo-Sanz:
A mixture of experts model for predicting persistent weather patterns. CoRR abs/1903.10012 (2019) - [i15]María Pérez-Ortiz, Peter Tiño, Rafal Mantiuk, César Hervás-Martínez:
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets. CoRR abs/1903.10022 (2019) - [i14]Peter Tiño:
Dynamical Systems as Temporal Feature Spaces. CoRR abs/1907.06382 (2019) - [i13]Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer:
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. CoRR abs/1912.04832 (2019) - 2018
- [j58]Peter Tiño:
Asymptotic Fisher memory of randomized linear symmetric Echo State Networks. Neurocomputing 298: 4-8 (2018) - [j57]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Supervised low rank indefinite kernel approximation using minimum enclosing balls. Neurocomputing 318: 213-226 (2018) - [c83]Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tiño:
Machine learning and data analysis in astroinformatics. ESANN 2018 - [c82]Claudio Gallicchio, Alessio Micheli, Peter Tiño:
Randomized Recurrent Neural Networks. ESANN 2018 - [c81]M. Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, Carlos Casanova-Mateo, Sancho Salcedo-Sanz:
A mixture of experts model for predicting persistent weather patterns. IJCNN 2018: 1-8 - [c80]Frank-Michael Schleif, Christoph Raab, Peter Tiño:
Sparsification of Indefinite Learning Models. S+SSPR 2018: 173-183 - 2017
- [j56]Fengzhen Tang, Peter Tiño:
Ordinal regression based on learning vector quantization. Neural Networks 93: 76-88 (2017) - [j55]Frank-Michael Schleif, Peter Tiño:
Indefinite Core Vector Machine. Pattern Recognit. 71: 187-195 (2017) - [c79]Sreejita Ghosh, Elizabeth Sarah Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tiño, Kerstin Bunte:
Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. ESANN 2017 - [c78]Peter Tiño:
Fisher memory of linear Wigner echo state networks. ESANN 2017 - [c77]Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño:
Self-Awareness for Dynamic Knowledge Management in Self-Adaptive Volunteer Services. ICWS 2017: 180-187 - [c76]Fani Tsapeli, Peter Tiño, Mirco Musolesi:
Probabilistic matching: Causal inference under measurement errors. IJCNN 2017: 278-285 - [c75]Luca Pasa, Alessandro Sperduti, Peter Tiño:
Linear dynamical based models for sequential domains. IJCNN 2017: 2201-2208 - [c74]Yuan Shen, Peter Tiño, Krasimira Tsaneva-Atanasova:
Classification of sparsely and irregularly sampled time series: A learning in model space approach. IJCNN 2017: 3696-3703 - [i12]Fani Tsapeli, Nikolaos Bezirgiannidis, Peter Tiño, Mirco Musolesi:
Linking Twitter Events With Stock Market Jitters. CoRR abs/1709.06519 (2017) - 2016
- [j54]Hanin H. Alahmadi, Yuan Shen, Shereen Fouad, Caroline Di Bernardi Luft, Peter Bentham, Zoe Kourtzi, Peter Tiño:
Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment. Frontiers Comput. Neurosci. 10: 117 (2016) - [j53]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer:
Model-coupled autoencoder for time series visualisation. Neurocomputing 192: 139-146 (2016) - [j52]María Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez:
Oversampling the Minority Class in the Feature Space. IEEE Trans. Neural Networks Learn. Syst. 27(9): 1947-1961 (2016) - [c73]Frank-Michael Schleif, Ata Kabán, Peter Tiño:
Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. ANNPR 2016: 42-54 - [c72]Frank-Michael Schleif, Peter Tiño, Yingyu Liang:
Learning in indefinite proximity spaces - recent trends. ESANN 2016 - [c71]Sultanah Al Otaibi, Peter Tiño, Somak Raychaudhury:
Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams. IDEAL 2016: 552-559 - [c70]Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño:
Interaction-Awareness for Self-Adaptive Volunteer Computing. SASO 2016: 148-149 - [c69]Nahed Alowadi, Yuan Shen, Peter Tiño:
Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data. WSOM 2016: 193-203 - [i11]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer:
Model-Coupled Autoencoder for Time Series Visualisation. CoRR abs/1601.05654 (2016) - [i10]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Probabilistic classifiers with low rank indefinite kernels. CoRR abs/1604.02264 (2016) - 2015
- [j51]Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen:
The Benefits of Modeling Slack Variables in SVMs. Neural Comput. 27(4): 954-981 (2015) - [j50]Frank-Michael Schleif, Peter Tiño:
Indefinite Proximity Learning: A Review. Neural Comput. 27(10): 2039-2096 (2015) - [c68]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra:
Autoencoding time series for visualisation. ESANN 2015 - [c67]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Probabilistic Classification Vector Machine at large scale. ESANN 2015 - [c66]Rafee T. Ibrahem, Peter Tiño, Richard J. Pearson, Trevor J. Ponman, Arif Babul:
Automated Detection of Galaxy Groups Through Probabilistic Hough Transform. ICONIP (3) 2015: 323-331 - [c65]Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño, Peter R. Lewis:
Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness. ICWS 2015: 57-64 - [c64]Huanhuan Chen, Fengzhen Tang, Peter Tiño, Anthony G. Cohn, Xin Yao:
Model Metric Co-Learning for Time Series Classification. IJCAI 2015: 3387-3394 - [c63]Frank-Michael Schleif, H. Chen, Peter Tiño:
Incremental probabilistic classification vector machine with linear costs. IJCNN 2015: 1-8 - [c62]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD 2015: 160-170 - [p2]Peter Tiño, Lubica Benuskova, Alessandro Sperduti:
Artificial Neural Network Models. Handbook of Computational Intelligence 2015: 455-471 - [i9]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra:
Autoencoding Time Series for Visualisation. CoRR abs/1505.00936 (2015) - 2014
- [j49]Huanhuan Chen, Peter Tiño, Xin Yao:
Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space. Comput. Chem. Eng. 67: 33-42 (2014) - [j48]Joseba Quevedo, Huanhuan Chen, Miquel Àngel Cugueró, Peter Tiño, Vicenç Puig, Diego García, Ramon Sarrate, Xin Yao:
Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network. Eng. Appl. Artif. Intell. 30: 18-29 (2014) - [j47]Yuan Shen, Stephen D. Mayhew, Zoe Kourtzi, Peter Tiño:
Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models. NeuroImage 84: 657-671 (2014) - [j46]Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez:
Ordinal regression neural networks based on concentric hyperspheres. Neural Networks 59: 51-60 (2014) - [j45]Jakub Mazgut, Peter Tiño, Mikael Bodén, Hong Yan:
Dimensionality reduction and topographic mapping of binary tensors. Pattern Anal. Appl. 17(3): 497-515 (2014) - [j44]Huanhuan Chen, Peter Tiño, Ali Rodan, Xin Yao:
Learning in the Model Space for Cognitive Fault Diagnosis. IEEE Trans. Neural Networks Learn. Syst. 25(1): 124-136 (2014) - [j43]Huanhuan Chen, Peter Tiño, Xin Yao:
Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection. IEEE Trans. Neural Networks Learn. Syst. 25(2): 356-369 (2014) - [c61]Frank-Michael Schleif, Peter Tiño, Thomas Villmann:
Recent trends in learning of structured and non-standard data. ESANN 2014 - [c60]Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen:
Support Vector Ordinal Regression using Privileged Information. ESANN 2014 - [c59]Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño:
Towards Self-Aware Service Composition. HPCC/CSS/ICESS 2014: 1275-1279 - [c58]Fengzhen Tang, Peter Tiño, Huanhuan Chen:
Learning the deterministically constructed Echo State Networks. IJCNN 2014: 77-83 - [c57]Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño, Peter R. Lewis:
A Utility Model for Volunteered Service Composition. UCC 2014: 337-344 - 2013
- [j42]Peter Tiño:
Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get? Entropy 15(4): 1202-1220 (2013) - [j41]Alessio Micheli, Frank-Michael Schleif, Peter Tiño:
Novel approaches in machine learning and computational intelligence. Neurocomputing 112: 1-3 (2013) - [j40]Peter Tiño, Ali Rodan:
Short term memory in input-driven linear dynamical systems. Neurocomputing 112: 58-63 (2013) - [j39]Nikolay I. Nikolaev, Peter Tiño, Evgueni N. Smirnov:
Time-dependent series variance learning with recurrent mixture density networks. Neurocomputing 122: 501-512 (2013) - [j38]Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez:
Exploitation of Pairwise Class Distances for Ordinal Classification. Neural Comput. 25(9): 2450-2485 (2013) - [j37]Orla M. Doyle, Krasimira Tsaneva-Atanasova, James Michael Harte, Paul A. Tiffin, Peter Tiño, Vanessa Díaz-Zuccarini:
Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models. IEEE Trans. Biomed. Eng. 60(3): 735-742 (2013) - [j36]Peter Tiño, Siang Yew Chong, Xin Yao:
Complex Coevolutionary Dynamics - Structural Stability and Finite Population Effects. IEEE Trans. Evol. Comput. 17(2): 155-164 (2013) - [j35]Weishan Dong, Tianshi Chen, Peter Tiño, Xin Yao:
Scaling Up Estimation of Distribution Algorithms for Continuous Optimization. IEEE Trans. Evol. Comput. 17(6): 797-822 (2013) - [j34]Shereen Fouad, Peter Tiño, Somak Raychaudhury, Petra Schneider:
Incorporating Privileged Information Through Metric Learning. IEEE Trans. Neural Networks Learn. Syst. 24(7): 1086-1098 (2013) - [j33]Phil Weber, Behzad Bordbar, Peter Tiño:
A Framework for the Analysis of Process Mining Algorithms. IEEE Trans. Syst. Man Cybern. Syst. 43(2): 303-317 (2013) - [c56]Phil Weber, Behzad Bordbar, Peter Tiño:
A principled approach to mining from noisy logs using Heuristics Miner. CIDM 2013: 119-126 - [c55]Shereen Fouad, Peter Tiño:
Ordinal-based metric learning for learning using privileged information. IJCNN 2013: 1-8 - [c54]Shuo Wang, Leandro L. Minku, Davide Ghezzi, Daniele Caltabiano, Peter Tiño, Xin Yao:
Concept drift detection for online class imbalance learning. IJCNN 2013: 1-10 - [c53]Huanhuan Chen, Fengzhen Tang, Peter Tiño, Xin Yao:
Model-based kernel for efficient time series analysis. KDD 2013: 392-400 - [c52]Yuan Shen, Stephen D. Mayhew, Zoe Kourtzi, Peter Tiño:
A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI Data. MICCAI (2) 2013: 657-664 - [i8]Boris Rudolf, Mária Markosová, Martin Cajági, Peter Tiño:
Degree distribution and scaling in the Connecting Nearest Neighbors model. CoRR abs/1304.3375 (2013) - 2012
- [j32]Richard Price, Peter Tiño, Georgios Theodoropoulos:
Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks. Mob. Networks Appl. 17(3): 378-394 (2012) - [j31]Ali Rodan, Peter Tiño:
Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps. Neural Comput. 24(7): 1822-1852 (2012) - [j30]Shereen Fouad, Peter Tiño:
Adaptive Metric Learning Vector Quantization for Ordinal Classification. Neural Comput. 24(11): 2825-2851 (2012) - [j29]Siang Yew Chong, Peter Tiño, Day Chyi Ku, Xin Yao:
Improving Generalization Performance in Co-Evolutionary Learning. IEEE Trans. Evol. Comput. 16(1): 70-85 (2012) - [c51]Manjunath Gandhi, Peter Tiño, Herbert Jaeger:
Theory of Input Driven Dynamical Systems. ESANN 2012 - [c50]Peter Tiño, Ali Rodan:
Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems. ESANN 2012 - [c49]Phil Weber, Peter Tiño, Behzad Bordbar:
Process Mining in Non-Stationary Environments. ESANN 2012 - [c48]Shereen Fouad, Peter Tiño, Somak Raychaudhury, Petra Schneider:
Learning Using Privileged Information in Prototype Based Models. ICANN (2) 2012: 322-329 - [c47]Shereen Fouad, Peter Tiño:
Prototype Based Modelling for Ordinal Classification. IDEAL 2012: 208-215 - [c46]Peter Tiño, Somak Raychaudhury:
Computational Intelligence in Astronomy - A Win-Win Situation. TPNC 2012: 57-71 - [i7]Huanhuan Chen, Peter Tiño, Xin Yao, Ali Rodan:
Learning in the Model Space for Fault Diagnosis. CoRR abs/1210.8291 (2012) - 2011
- [j28]Peter Tiño, Hongya Zhao, Hong Yan:
Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform. IEEE ACM Trans. Comput. Biol. Bioinform. 8(4): 1093-1107 (2011) - [j27]Ali Rodan, Peter Tiño:
Minimum Complexity Echo State Network. IEEE Trans. Neural Networks 22(1): 131-144 (2011) - [c45]Ali Rodan, Peter Tiño:
Negatively Correlated Echo State Networks. ESANN 2011 - [c44]Nikolay I. Nikolaev, Peter Tiño, Evgueni N. Smirnov:
Time-Dependent Series Variance Estimation via Recurrent Neural Networks. ICANN (1) 2011: 176-184 - [c43]Phil Weber, Behzad Bordbar, Peter Tiño:
Real-Time Detection of Process Change using Process Mining. ICCSW 2011: 108-114 - [c42]Phil Weber, Behzad Bordbar, Peter Tiño:
A Principled Approach to the Analysis of Process Mining Algorithms. IDEAL 2011: 474-481 - [c41]Peter Tiño:
One-Shot Learning of Poisson Distributions in Serial Analysis of Gene Expression. ISNN (2) 2011: 37-46 - [c40]Jakub Mazgut, Martina Paulinyová, Peter Tiño:
Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis. MEMICS 2011: 146-154 - [i6]Weishan Dong, Tianshi Chen, Peter Tiño, Xin Yao:
Scaling Up Estimation of Distribution Algorithms For Continuous Optimization. CoRR abs/1111.2221 (2011) - 2010
- [j26]Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury, Xin Yao, Markus Harva:
Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application. Pattern Recognit. 43(3): 1165-1179 (2010) - [c39]Peter Tiño, Siang Yew Chong, Xin Yao:
On Reliability Of Simulations Of Complex Co-Evolutionary Processes. ECMS 2010: 258-264 - [c38]Jakub Mazgut, Peter Tiño, Mikael Bodén, Hong Yan:
Multilinear Decomposition and Topographic Mapping of Binary Tensors. ICANN (1) 2010: 317-326 - [c37]Ali Rodan, Peter Tiño:
Simple Deterministically Constructed Recurrent Neural Networks. IDEAL 2010: 267-274 - [c36]Richard Price, Peter Tiño:
Adapting to NAT timeout values in P2P overlay networks. IPDPS Workshops 2010: 1-6 - [e3]Colin Fyfe, Peter Tiño, Darryl Charles, César Ignacio García-Osorio, Hujun Yin:
Intelligent Data Engineering and Automated Learning - IDEAL 2010, 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Lecture Notes in Computer Science 6283, Springer 2010, ISBN 978-3-642-15380-8 [contents] - [i5]Peter Tiño:
One-shot Learning of Poisson Distributions in fast changing environments. Learning paradigms in dynamic environments 2010
2000 – 2009
- 2009
- [j25]Peter Tiño:
Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays. BMC Bioinform. 10: 310 (2009) - [j24]Siang Yew Chong, Peter Tiño, Xin Yao:
Relationship Between Generalization and Diversity in Coevolutionary Learning. IEEE Trans. Comput. Intell. AI Games 1(3): 214-232 (2009) - [j23]Huanhuan Chen, Peter Tiño, Xin Yao:
Predictive Ensemble Pruning by Expectation Propagation. IEEE Trans. Knowl. Data Eng. 21(7): 999-1013 (2009) - [j22]Huanhuan Chen, Peter Tiño, Xin Yao:
Probabilistic Classification Vector Machines. IEEE Trans. Neural Networks 20(6): 901-914 (2009) - [c35]Richard Price, Peter Tiño:
Still alive: Extending keep-alive intervals in P2P overlay networks. CollaborateCom 2009: 1-10 - [c34]Nikolaos Gianniotis, Peter Tiño:
Visualization of Structured Data via Generative Probabilistic Modeling. Similarity-Based Clustering 2009: 118-137 - [c33]Nikolaos Gianniotis, Peter Tiño, Steve Spreckley, Somak Raychaudhury:
Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. ICANN (1) 2009: 567-576 - [c32]Peter Tiño, Hongya Zhao, Hong Yan:
Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data. IJCBS 2009: 48-51 - [c31]Xiaoxia Wang, Peter Tiño, Mark A. Fardal, Somak Raychaudhury, Arif Babul:
Fast parzen window density estimator. IJCNN 2009: 3267-3274 - [i4]Peter Tiño, Juan Carlos Cuevas-Tello, Somak Raychaudhury:
Estimating Time Delay in Gravitationally Lensed Fluxes. Similarity-based learning on structures 2009 - [i3]Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury, Xin Yao, Markus Harva:
Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application. CoRR abs/0908.3706 (2009) - 2008
- [j21]Siang Yew Chong, Peter Tiño, Xin Yao:
Measuring Generalization Performance in Coevolutionary Learning. IEEE Trans. Evol. Comput. 12(4): 479-505 (2008) - [j20]Nikolaos Gianniotis, Peter Tiño:
Visualization of Tree-Structured Data Through Generative Topographic Mapping. IEEE Trans. Neural Networks 19(8): 1468-1493 (2008) - [c30]Michal Cernanský, Peter Tiño:
Predictive Modeling with Echo State Networks. ICANN (1) 2008: 778-787 - [c29]Xiaoxia Wang, Peter Tiño, Mark A. Fardal:
Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model. ECML/PKDD (2) 2008: 566-581 - [i2]Peter Tiño:
Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Recurrent Neural Networks 2008 - 2007
- [j19]Peter Tiño:
Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Neural Comput. 19(4): 1056-1081 (2007) - [c28]Nikolaos Gianniotis, Peter Tiño:
Visualisation of tree-structured data through generative probabilistic modelling. ESANN 2007: 97-102 - [c27]Michal Cernanský, Peter Tiño:
Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences. ICANN (1) 2007: 618-627 - [c26]Peter Tiño:
Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks. ICONIP (1) 2007: 405-414 - [c25]Peter Tiño, Nikolaos Gianniotis:
Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling. IJCAI 2007: 1083-1088 - [c24]Peter Tiño:
On Conditions for Intermittent Search in Self-organizing Neural Networks. MICAI 2007: 172-181 - [p1]Peter Tiño, Barbara Hammer, Mikael Bodén:
Markovian Bias of Neural-based Architectures With Feedback Connections. Perspectives of Neural-Symbolic Integration 2007: 95-133 - [e2]Hujun Yin, Peter Tiño, Emilio Corchado, William Byrne, Xin Yao:
Intelligent Data Engineering and Automated Learning - IDEAL 2007, 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings. Lecture Notes in Computer Science 4881, Springer 2007, ISBN 978-3-540-77225-5 [contents] - 2006
- [j18]Peter Tiño, Ashley J. S. Mills:
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. Neural Comput. 18(3): 591-613 (2006) - [j17]Peter Tiño, Igor Farkas, Jort van Mourik:
Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Comput. 18(10): 2529-2567 (2006) - [c23]Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury:
A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses. ECML 2006: 614-621 - [c22]Huanhuan Chen, Peter Tiño, Xin Yao:
A Probabilistic Ensemble Pruning Algorithm. ICDM Workshops 2006: 878-882 - [c21]Jane M. Binner, Barry Jones, Graham Kendall, Jonathan A. Tepper, Peter Tiño:
Does Money Matter? An Artificial Intelligence Approach. JCIS 2006 - [c20]Peter Tiño:
Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. PPSN 2006: 633-640 - [i1]Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury:
How accurate are the time delay estimates in gravitational lensing? CoRR abs/astro-ph/0605042 (2006) - 2005
- [j16]Gavin Brown, Jeremy L. Wyatt, Peter Tiño:
Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. 6: 1621-1650 (2005) - [j15]Ian T. Nabney, Yi Sun, Peter Tiño, Ata Kabán:
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization. IEEE Trans. Knowl. Data Eng. 17(3): 384-400 (2005) - [c19]Peter Tiño, Ashley J. S. Mills:
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. ICNC (2) 2005: 666-675 - [c18]Peter Tiño, Igor Farkas:
On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. ICNC (2) 2005: 676-685 - [c17]Peter Tiño, Igor Farkas, Jort van Mourik:
Recursive Self-organizing Map as a Contractive Iterative Function System. IDEAL 2005: 327-334 - 2004
- [j14]Peter Tiño, Ian T. Nabney, Bruce S. Williams, Jens Lösel, Yi Sun:
Nonlinear Prediction of Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 44(5): 1647-1653 (2004) - [j13]Gabriela Polcicová, Peter Tiño:
Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns. Neural Networks 17(8-9): 1183-1199 (2004) - [j12]Peter Tiño, Michal Cernanský, Lubica Benusková:
Markovian architectural bias of recurrent neural networks. IEEE Trans. Neural Networks 15(1): 6-15 (2004) - [c16]Peter Tiño, Barbara Hammer:
On Early Stages of Learning in Connectionist Models with Feedback Connections. AAAI Technical Report (3) 2004: 69-71 - [c15]Gabriela Polcicová, Peter Tiño:
Introducing a Star Topology into Latent Class Models for Collaborative Filtering. AIAI 2004: 293-303 - [c14]Peter Tiño, Ata Kabán, Yi Sun:
A generative probabilistic approach to visualizing sets of symbolic sequences. KDD 2004: 701-706 - [c13]Richard Price, Peter Tiño:
Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies. PPSN 2004: 982-990 - [e1]Xin Yao, Edmund K. Burke, José Antonio Lozano, Jim Smith, Juan Julián Merelo Guervós, John A. Bullinaria, Jonathan E. Rowe, Peter Tiño, Ata Kabán, Hans-Paul Schwefel:
Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, Birmingham, UK, September 18-22, 2004, Proceedings. Lecture Notes in Computer Science 3242, Springer 2004, ISBN 3-540-23092-0 [contents] - 2003
- [j11]Barbara Hammer, Peter Tiño:
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Comput. 15(8): 1897-1929 (2003) - [j10]Peter Tiño, Barbara Hammer:
Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Comput. 15(8): 1931-1957 (2003) - 2002
- [j9]Peter Tiño, Ian T. Nabney:
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way. IEEE Trans. Pattern Anal. Mach. Intell. 24(5): 639-656 (2002) - [c12]Peter Tiño, Barbara Hammer:
Architectural Bias in Recurrent Neural Networks - Fractal Analysis. ICANN 2002: 1359-1364 - [c11]Ata Kabán, Peter Tiño, Mark A. Girolami:
A General Framework for a Principled Hierarchical Visualization of Multivariate Data. IDEAL 2002: 518-523 - 2001
- [j8]Peter Tiño, Georg Dorffner:
Predicting the Future of Discrete Sequences from Fractal Representations of the Past. Mach. Learn. 45(2): 187-217 (2001) - [j7]Peter Tiño, Bill G. Horne, C. Lee Giles:
Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks). Neural Comput. 13(6): 1379-1414 (2001) - [j6]Peter Tiño, Christian Schittenkopf, Georg Dorffner:
Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series. Pattern Anal. Appl. 4(4): 283-299 (2001) - [j5]Peter Tiño, Christian Schittenkopf, Georg Dorffner:
Financial volatility trading using recurrent neural networks. IEEE Trans. Neural Networks 12(4): 865-874 (2001) - [c10]Peter Tiño, Ian T. Nabney, Yi Sun:
Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds. ICANN 2001: 421-428 - 2000
- [c9]Christian Schittenkopf, Peter Tiño, Georg Dorffner:
The profitability of trading volatility using real-valued and symbolic models. CIFEr 2000: 8-11 - [c8]Peter Tiño, Michal Stancík, Lubica Benusková:
Building Predictive Models on Complex Symbolic Sequences with a Second-Order Recurrent BCM Network with Lateral Inhibition. IJCNN (2) 2000: 265-270
1990 – 1999
- 1999
- [j4]Peter Tiño, Miroslav Koteles:
Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences. IEEE Trans. Neural Networks 10(2): 284-302 (1999) - [j3]Peter Tiño:
Spatial representation of symbolic sequences through iterative function systems. IEEE Trans. Syst. Man Cybern. Part A 29(4): 386-393 (1999) - [c7]Shan Parfitt, Peter Tiño, Georg Dorffner:
Graded Grammaticality in Prediction Fractal Machines. NIPS 1999: 52-58 - [c6]Peter Tiño, Georg Dorffner:
Building Predictive Models from Fractal Representations of Symbolic Sequences. NIPS 1999: 645-651 - 1998
- [c5]Peter Tiño, Georg Dorffner, Christian Schittenkopf:
Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics. Hybrid Neural Systems 1998: 255-269 - [c4]Peter Tiño, Georg Dorffner:
Recurrent Neural Networks with Iterated Function Systems Dynamics. NC 1998: 526-532 - 1997
- [c3]Peter Tiño, V. Vojtek:
Modeling Complex Symbolic Sequences with Neural Based Systems. ICANNGA 1997: 459-463 - [c2]Peter Tiño, V. Vojtek:
Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences. KES (2) 1997: 551-558 - 1996
- [j2]Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles:
Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans. Neural Networks 7(6): 1329-1338 (1996) - 1995
- [j1]Peter Tiño, Jozef Sajda:
Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model. Neural Comput. 7(4): 822-844 (1995) - [c1]Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles:
Learning long-term dependencies is not as difficult with NARX networks. NIPS 1995: 577-583
Coauthor Index
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