Data mining, Minimum description length, Artificial intelligence, Theoretical computer science and Algorithm are his primary areas of study. His Data mining research includes themes of Bayesian information criterion, Principle of maximum entropy, Heuristic, Transaction data and Probabilistic logic. His work deals with themes such as Key and Benchmark data, which intersect with Bayesian information criterion.
His Artificial intelligence research integrates issues from Relational database, Categorical variable and Pattern recognition. His Theoretical computer science study combines topics from a wide range of disciplines, such as Construct, Graph theory, Spotting and Graph. His Algorithm study often links to related topics such as Classifier.
Jilles Vreeken focuses on Data mining, Artificial intelligence, Theoretical computer science, Minimum description length and Algorithm. Jilles Vreeken combines subjects such as Principle of maximum entropy, Key and Multivariate statistics with his study of Data mining. His research investigates the connection between Artificial intelligence and topics such as Machine learning that intersect with problems in Estimator.
Jilles Vreeken has researched Theoretical computer science in several fields, including Visualization, Constraint and Graph. Jilles Vreeken usually deals with Minimum description length and limits it to topics linked to Kolmogorov complexity and Causal inference and Margin. His Algorithm study integrates concerns from other disciplines, such as Random variable and Joint probability distribution.
Jilles Vreeken spends much of his time researching Theoretical computer science, Machine learning, Artificial intelligence, Estimator and Algorithm. Jilles Vreeken has included themes like Minimum description length, Spurious relationship, Contrast and Graph embedding in his Theoretical computer science study. Jilles Vreeken works mostly in the field of Minimum description length, limiting it down to topics relating to Kolmogorov complexity and, in certain cases, Margin, as a part of the same area of interest.
His study in the field of Knowledge graph also crosses realms of Improved performance, Model test and Class. His Algorithm research incorporates themes from Principle of maximum entropy, Event, Reliability and Heuristic. His Functional dependency study is related to the wider topic of Data mining.
His scientific interests lie mostly in Machine learning, Artificial intelligence, Principle of maximum entropy, Algorithm and Theoretical computer science. His work on Errors-in-variables models as part of general Machine learning research is frequently linked to Model test, Improved performance and Class, thereby connecting diverse disciplines of science. His work on Question answering as part of general Artificial intelligence research is frequently linked to Context, bridging the gap between disciplines.
As a member of one scientific family, Jilles Vreeken mostly works in the field of Principle of maximum entropy, focusing on Data modeling and, on occasion, Joint probability distribution. The various areas that Jilles Vreeken examines in his Algorithm study include Entropy, Maximum entropy probability distribution and Relaxation. His studies deal with areas such as Mixture model and Graph as well as Theoretical computer science.
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Krimp: mining itemsets that compress
Jilles Vreeken;Matthijs Leeuwen;Arno Siebes.
Data Mining and Knowledge Discovery (2011)
Intelligent Traffic Light Control
Wiering;J. Veenen;J. Vreeken;A. Koopman.
Spotting Culprits in Epidemics: How Many and Which Ones?
B. Aditya Prakash;Jilles Vreeken;Christos Faloutsos.
international conference on data mining (2012)
Item Sets that Compress.
Arno Siebes;Jilles Vreeken;Matthijs van Leeuwen.
siam international conference on data mining (2006)
Simulation and optimization of traffic in a city
M. Wiering;J. Vreeken;J. van Veenen;A. Koopman.
ieee intelligent vehicles symposium (2004)
Spiking neural networks, an introduction
Tell me what i need to know: succinctly summarizing data with itemsets
Michael Mampaey;Nikolaj Tatti;Jilles Vreeken.
knowledge discovery and data mining (2011)
The long and the short of it: summarising event sequences with serial episodes
Nikolaj Tatti;Jilles Vreeken.
knowledge discovery and data mining (2012)
Fast and reliable anomaly detection in categorical data
Leman Akoglu;Hanghang Tong;Jilles Vreeken;Christos Faloutsos.
conference on information and knowledge management (2012)
Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks
Kumaripaba Athukorala;Dorota Głowacka;Giulio Jacucci;Antti Oulasvirta.
association for information science and technology (2016)
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