Peter Tino - researchr alias

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Viewing Publication 1 - 74 from 74

2023

An Approach for Dynamic Behavioural Prediction and Fault Injection in Cyber-Physical SystemsHayatullahi Bolaji Adeyemo, Rami Bahsoon, Peter Tino. bdc 2023: [doi]

Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List SchedulingVahab Samandi, Peter Tino, Rami Bahsoon. HAIS 2023: 244-255 [doi]

LAAT: Locally Aligned Ant Technique for Discovering Multiple Faint Low Dimensional Structures of Varying DensityAbolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tino. tkde, 35(6):6014-6027, June 2023. [doi]

Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite ManifoldFengzhen Tang, Peter Tino, Haibin Yu. tcyb, 53(8):5178-5190, 2023. [doi]

Hierarchical Reduced-Space Drift Detection Framework for Multivariate Supervised Data StreamsShuyi Zhang, Peter Tino, Xin Yao 0001. tkde, 35(3):2628-2640, March 2023. [doi]

2022

Population-Based Optimization on Riemannian ManifoldsRobert Simon Fong, Peter Tino. Volume 1046 of Studies in Computational Intelligence, Springer, 2022. [doi]

Spatio-Temporal Activity Recognition for Evolutionary Search Behavior PredictionStephen Friess, Peter Tino, Stefan Menzel, Zhao Xu 0001, Bernhard Sendhoff, Xin Yao 0001. ijcnn 2022: 1-8 [doi]

Manifold Alignment Aware Ants: A Markovian Process for Manifold ExtractionMohammad Mohammadi, Peter Tino, Kerstin Bunte. neco, 34(3):595-641, 2022. [doi]

Greedy Selection of the Torso Electrodes for the Solution of Inverse Problem with a Single DipoleBeata Ondrusova, Jana Svehlíková, Milan Tysler, Peter Tino. cinc 2022: 1-4 [doi]

Duplication Scheduling with Bottom-Up Top-Down Recursive Neural NetworkVahab Samandi, Peter Tino, Rami Bahsoon. ideal 2022: 170-178 [doi]

ASAP - A sub-sampling approach for preserving topological structures modeled with geodesic topographic mappingAbolfazl Taghribi, Marco Canducci, Michele Mastropietro, Sven De Rijcke, Kerstin Bunte, Peter Tino. ijon, 470:376-388, 2022. [doi]

Input-to-State Representation in Linear Reservoirs DynamicsPietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tino. tnn, 33(9):4598-4609, 2022. [doi]

2021

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical SimulationsMarco Canducci, Abolfazl Taghribi, Michele Mastropietro, Sven De Rijcke, Reynier Peletier, Kerstin Bunte, Peter Tino. ideal 2021: 493-501 [doi]

SOMiMS - Topographic Mapping in the Model SpaceXinyue Chen, Yuan Shen, Eder Zavala, Krasimira Tsaneva-Atanasova, Thomas Upton, Georgina Russell, Peter Tino. ideal 2021: 502-510 [doi]

Attaining Meta-self-awareness through Assessment of Quality-of-KnowledgeAbdessalam Elhabbash, Rami Bahsoon, Peter Tino, Peter R. Lewis, Yehia Elkhatib. icws 2021: 712-723 [doi]

Predicting CMA-ES Operators as Inductive Biases for Shape Optimization ProblemsStephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao 0001. ssci 2021: 1-7 [doi]

Designing Robust Models for Behaviour Prediction Using Sparse Data from Mobile Sensing: A Case Study of Office Workers' Availability for Well-being InterventionsSeyma Kucukozer-Cavdar, Tugba Taskaya-Temizel, Abhinav Mehrotra, Mirco Musolesi, Peter Tino. health, 2(4), 2021. [doi]

Model-Based Relevance of Measuring Electrodes for the Inverse Solution with a Single DipoleBeata Ondrusova, Jana Svehlíková, Jan Zelinka, Milan Tysler, Peter Tino. cinc 2021: 1-4 [doi]

Probabilistic learning vector quantization on manifold of symmetric positive definite matricesFengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji. NN, 142:105-118, 2021. [doi]

Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution DetectionShuyi Zhang, Chao Pan, Liyan Song, Xiaoyu Wu, Zheng Hu, Ke Pei, Peter Tino, Xin Yao 0001. pkdd 2021: 795-810 [doi]

2014

Learning in the Model Space for Cognitive Fault DiagnosisHuanhuan Chen, Peter Tino, Ali Rodan, Xin Yao. tnn, 25(1):124-136, 2014. [doi]

Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model spaceHuanhuan Chen, Peter Tino, Xin Yao. cce, 67:33-42, 2014. [doi]

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function SelectionHuanhuan Chen, Peter Tino, Xin Yao. tnn, 25(2):356-369, 2014. [doi]

Ordinal regression neural networks based on concentric hyperspheresPedro Antonio Gutiérrez, Peter Tino, César Hervás-Martínez. NN, 59:51-60, 2014. [doi]

Dimensionality reduction and topographic mapping of binary tensorsJakub Mazgut, Peter Tino, Mikael Bodén, Hong Yan. paa, 17(3):497-515, 2014. [doi]

Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process modelsYuan Shen, Stephen D. Mayhew, Zoe Kourtzi, Peter Tino. neuroimage, 84:657-671, 2014. [doi]

2013

Model-based kernel for efficient time series analysisHuanhuan Chen, Fengzhen Tang, Peter Tino, Xin Yao. kdd 2013: 392-400 [doi]

Scaling Up Estimation of Distribution Algorithms for Continuous OptimizationWeishan Dong, Tianshi Chen, Peter Tino, Xin Yao. tec, 17(6):797-822, 2013. [doi]

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive ModelsOrla M. Doyle, Krasimira Tsaneva-Atansaova, James Harte, Paul A. Tiffin, Peter Tino, Vanessa Díaz-Zuccarini. tbe, 60(3):735-742, 2013. [doi]

Ordinal-based metric learning for learning using privileged informationShereen Fouad, Peter Tino. ijcnn 2013: 1-8 [doi]

Incorporating Privileged Information Through Metric LearningShereen Fouad, Peter Tino, Somak Raychaudhury, Petra Schneider. tnn, 24(7):1086-1098, 2013. [doi]

Novel approaches in machine learning and computational intelligenceAlessio Micheli, Frank-Michael Schleif, Peter Tino. ijon, 112:1-3, 2013. [doi]

Time-dependent series variance learning with recurrent mixture density networksNikolay I. Nikolaev, Peter Tino, Evgueni N. Smirnov. ijon, 122:501-512, 2013. [doi]

A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI DataYuan Shen, Stephen D. Mayhew, Zoe Kourtzi, Peter Tino. miccai 2013: 657-664 [doi]

Exploitation of Pairwise Class Distances for Ordinal ClassificationJavier Sánchez-Monedero, Pedro Antonio Gutiérrez, Peter Tino, César Hervás-Martínez. neco, 25(9):2450-2485, 2013. [doi]

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get?Peter Tino. entropy, 15(4):1202-1220, 2013. [doi]

Short term memory in input-driven linear dynamical systemsPeter Tino, Ali Rodan. ijon, 112:58-63, 2013. [doi]

Complex Coevolutionary Dynamics - Structural Stability and Finite Population EffectsPeter Tino, Siang Yew Chong, Xin Yao. tec, 17(2):155-164, 2013. [doi]

Concept drift detection for online class imbalance learningShuo Wang, Leandro L. Minku, Davide Ghezzi, Daniele Caltabiano, Peter Tino, Xin Yao. ijcnn 2013: 1-10 [doi]

A principled approach to mining from noisy logs using Heuristics MinerPhil Weber, Behzad Bordbar, Peter Tino. cidm 2013: 119-126 [doi]

2012

Improving Generalization Performance in Co-Evolutionary LearningSiang Yew Chong, Peter Tino, Day Chyi Ku, Xin Yao. tec, 16(1):70-85, 2012. [doi]

Learning Using Privileged Information in Prototype Based ModelsShereen Fouad, Peter Tino, Somak Raychaudhury, Petra Schneider. icann 2012: 322-329 [doi]

Prototype Based Modelling for Ordinal ClassificationShereen Fouad, Peter Tino. ideal 2012: 208-215 [doi]

Adaptive Metric Learning Vector Quantization for Ordinal ClassificationShereen Fouad, Peter Tino. neco, 24(11):2825-2851, 2012. [doi]

Still Alive: Extending Keep-Alive Intervals in P2P Overlay NetworksRichard Price, Peter Tino, Georgios K. Theodoropoulos. monet, 17(3):378-394, 2012. [doi]

Simple Deterministically Constructed Cycle Reservoirs with Regular JumpsAli Rodan, Peter Tino. neco, 24(7):1822-1852, 2012. [doi]

Computational Intelligence in Astronomy - A Win-Win SituationPeter Tino, Somak Raychaudhury. tpnc 2012: 57-71 [doi]

2011

2010

2009

2008

Measuring Generalization Performance in Coevolutionary LearningSiang Yew Chong, Peter Tino, Xin Yao. tec, 12(4):479-505, 2008. [doi]

Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural NetworksPeter Tino. Dagstuhl 2008: [doi]

2007

Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic SequencesMichal Cernanský, Peter Tino. icann 2007: 618-627 [doi]

Visualisation of tree-structured data through generative probabilistic modellingNikolaos Gianniotis, Peter Tino. esann 2007: 97-102 [doi]

Markovian Bias of Neural-based Architectures With Feedback ConnectionsPeter Tino, Barbara Hammer, Mikael Bodén. In Barbara Hammer, Pascal Hitzler, editors, Perspectives of Neural-Symbolic Integration. Volume 77 of Studies in Computational Intelligence, pages 95-133, Springer, 2007. [doi]

Metric Properties of Structured Data Visualizations through Generative Probabilistic ModelingPeter Tino, Nikolaos Gianniotis. IJCAI 2007: 1083-1088 [doi]

On Conditions for Intermittent Search in Self-organizing Neural NetworksPeter Tino. micai 2007: 172-181 [doi]

Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural NetworksPeter Tino. neco, 19(4):1056-1081, 2007. [doi]

2006

Critical Temperatures for Intermittent Search in Self-Organizing Neural NetworksPeter Tino. ppsn 2006: 633-640 [doi]

2004

Markovian architectural bias of recurrent neural networksPeter Tino, Michal Cernanský, Lubica Benusková. tnn, 15(1):6-15, 2004. [doi]

2000

The profitability of trading volatility using real-valued and symbolic modelsChristian Schittenkopf, Peter Tino, Georg Dorffner. cifer 2000: 8-11 [doi]

1999

Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequencesPeter Tino, Miroslav Koteles. tnn, 10(2):284-302, 1999. [doi]

1996

Learning long-term dependencies in NARX recurrent neural networksTsungnan Lin, Bill G. Horne, Peter Tino, C. Lee Giles. tnn, 7(6):1329-1338, 1996. [doi]

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