Nikos Vlassis mainly investigates Artificial intelligence, Mathematical optimization, Reinforcement learning, Cluster analysis and Markov decision process. Nikos Vlassis combines subjects such as Expectation–maximization algorithm, Management science and Pattern recognition with his study of Artificial intelligence. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Mixture model, Partially observable Markov decision process, Value and Point.
His study in Reinforcement learning is interdisciplinary in nature, drawing from both Multi-agent system and Nash equilibrium. The study incorporates disciplines such as Applied mathematics, Data mining, Identification and Bioinformatics in addition to Cluster analysis. His work carried out in the field of Markov decision process brings together such families of science as Scale, Finite set and Bellman equation.
His primary areas of study are Artificial intelligence, Mathematical optimization, Algorithm, Markov decision process and Partially observable Markov decision process. His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Pattern recognition. His work on Bellman equation as part of general Mathematical optimization research is frequently linked to Set, bridging the gap between disciplines.
Nikos Vlassis interconnects Mixture model, Variable elimination and Expectation–maximization algorithm in the investigation of issues within Algorithm. His Markov decision process research is multidisciplinary, relying on both Computational complexity theory and State. His studies in Partially observable Markov decision process integrate themes in fields like Value, Function and Dynamic programming.
His main research concerns Data mining, Mathematical optimization, Estimator, Set and Artificial intelligence. His work in the fields of Data mining, such as Identification and Visualization, overlaps with other areas such as Motif and Spectral sequence. His biological study spans a wide range of topics, including Sampling, Regret, Bayesian probability, Sample and Reinforcement learning.
His study explores the link between Estimator and topics such as Causal inference that cross with problems in Estimation, Asymptotic distribution and Outcome. He studied Artificial intelligence and Machine learning that intersect with Human–computer interaction and Data set. His research in Algorithm intersects with topics in Image segmentation, Correspondence problem, Key and Active shape model.
His primary scientific interests are in Data mining, Theoretical computer science, Metabolomics, Identification and Range. His work on Visualization as part of general Data mining research is often related to Metagenomics, thus linking different fields of science. His Visualization study integrates concerns from other disciplines, such as Sequence, Computational biology, Identification and Bioinformatics.
Along with Theoretical computer science, other disciplines of study including Gap filling, Organism, Consistency, KEGG and Network analysis are integrated into his research. His Identification research incorporates elements of Mac OS, Software implementation, Cluster analysis and Pattern recognition. Nikos Vlassis has included themes like Disjoint sets, Biological system, Mathematical structure and Computational model in his Range study.
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The global k-means clustering algorithm
Aristidis Likas;Nikos A. Vlassis;Jakob J. Verbeek.
Pattern Recognition (2003)
Perseus: randomized point-based value iteration for POMDPs
Matthijs T. J. Spaan;Nikos Vlassis.
Journal of Artificial Intelligence Research (2005)
Efficient greedy learning of Gaussian mixture models
J. J. Verbeek;N. Vlassis;B. Kröse.
Neural Computation (2003)
A Greedy EM Algorithm for Gaussian Mixture Learning
Nikos Vlassis;Aristidis Likas.
Neural Processing Letters (2002)
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
Jelle R. Kok;Nikos Vlassis.
Journal of Machine Learning Research (2006)
VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data.
Cedric Christian Laczny;Tomasz Sternal;Valentin Plugaru;Piotr Gawron.
An analytic solution to discrete Bayesian reinforcement learning
Pascal Poupart;Nikos Vlassis;Jesse Hoey;Kevin Regan.
international conference on machine learning (2006)
Point-Based Value Iteration for Continuous POMDPs
Josep M. Porta;Nikos Vlassis;Matthijs T.J. Spaan;Pascal Poupart.
Journal of Machine Learning Research (2006)
Optimal and approximate Q-value functions for decentralized POMDPs
Frans A. Oliehoek;Matthijs T. J. Spaan;Nikos Vlassis.
Journal of Artificial Intelligence Research (2008)
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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