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ORCIDPeter Tiño
Peter Tino
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- affiliation: University of Birmingham, School of Computer Science
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2020 – today
- 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) - [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]