Hendrik Blockeel mainly focuses on Artificial intelligence, Machine learning, Inductive logic programming, Decision tree and Statistical relational learning. His biological study spans a wide range of topics, including Tree and Data mining. Hendrik Blockeel combines subjects such as Gene expression profiling, Genome, Gene, Genomics and DNA microarray with his study of Machine learning.
His Inductive logic programming research is multidisciplinary, relying on both Theoretical computer science, Representation, Set, Cluster analysis and Discretization. His work carried out in the field of Decision tree brings together such families of science as Tilde and Top-down and bottom-up design. His research in Statistical relational learning focuses on subjects like Active learning, which are connected to Statistical hypothesis testing, Reusability, Generalizability theory and Repeatability.
Hendrik Blockeel focuses on Artificial intelligence, Machine learning, Data mining, Cluster analysis and Inductive logic programming. His research in Artificial intelligence intersects with topics in Relational database, Statistical relational learning and Pattern recognition. His studies deal with areas such as Tree, Context and Set as well as Machine learning.
His Data mining study frequently links to related topics such as Data science. His Inductive logic programming research incorporates elements of Theoretical computer science, Logic programming, Inductive programming and Natural language processing. As a part of the same scientific study, he usually deals with the Theoretical computer science, concentrating on Inference and frequently concerns with Algorithm.
Artificial intelligence, Machine learning, Cluster analysis, Data mining and Statistical relational learning are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Context, Relational database and Pattern recognition. The study incorporates disciplines such as Tree and Identification in addition to Machine learning.
His work in the fields of Correlation clustering overlaps with other areas such as Series. His research in the fields of Data stream mining overlaps with other disciplines such as Drug. His work investigates the relationship between Statistical relational learning and topics such as Feature learning that intersect with problems in Theoretical computer science, Autoencoder, Representation, Representation and Artificial neural network.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Cluster analysis, Data mining and Statistical relational learning. Algorithmic learning theory and Inductive logic programming are among the areas of Artificial intelligence where the researcher is concentrating his efforts. His work on Canopy clustering algorithm, CURE data clustering algorithm and Constrained clustering as part of his general Machine learning study is frequently connected to Focus, thereby bridging the divide between different branches of science.
Hendrik Blockeel has included themes like Pairwise comparison and Constraint in his Cluster analysis study. His work is dedicated to discovering how Pairwise comparison, Construct are connected with Theoretical computer science and other disciplines. His studies in Data mining integrate themes in fields like Data science, Variety, Concept mining and Early results.
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Web mining research: a survey
Raymond Kosala;Hendrik Blockeel.
Sigkdd Explorations (2000)
Top-down induction of first-order logical decision trees
Hendrik Blockeel;Luc De Raedt.
Artificial Intelligence (1998)
Decision trees for hierarchical multi-label classification
Celine Vens;Jan Struyf;Leander Schietgat;Sašo Džeroski.
Machine Learning (2008)
Top-Down Induction of Clustering Trees
Hendrik Blockeel;Luc De Raedt;Jan Ramon.
international conference on machine learning (1998)
Relational Reinforcement Learning
Saso Dzeroski;Luc De Raedt;Hendrik Blockeel.
inductive logic programming (1998)
Knowledge Discovery in Databases: PKDD 2003
Nada Lavrač;Dragan Gamberger;Ljupčo Todorovski;Hendrik Blockeel.
Predicting gene function using hierarchical multi-label decision tree ensembles
Leander Schietgat;Celine Vens;Jan Struyf;Hendrik Blockeel.
BMC Bioinformatics (2010)
Decision trees for hierarchical multilabel classification: a case study in functional genomics
Hendrik Blockeel;Leander Schietgat;Jan Struyf;Sašo Džeroski.
european conference on principles of data mining and knowledge discovery (2006)
Efficient algorithms for decision tree cross-validation
Hendrik Blockeel;Jan Struyf.
Journal of Machine Learning Research (2003)
Improving the efficiency of inductive logic programming through the use of query packs
Hendrik Blockeel;Luc Dehaspe;Bart Demoen;Gerda Janssens.
Journal of Artificial Intelligence Research (2002)
(Impact Factor: 5.414)
Profile was last updated on December 6th, 2021.
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