2023 - Research.com Computer Science in New Zealand Leader Award
2022 - Research.com Computer Science in New Zealand Leader Award
Bernhard Pfahringer mostly deals with Artificial intelligence, Machine learning, Data mining, Data stream mining and Software. Bernhard Pfahringer focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Pattern recognition and, in some cases, Scheme, Pruning, Probability density function and Process. As part of the same scientific family, Bernhard Pfahringer usually focuses on Machine learning, concentrating on Classifier and intersecting with Data analysis.
His study in Data mining is interdisciplinary in nature, drawing from both Ensemble learning, Boosting and Experimental data. His research brings together the fields of World Wide Web and Software. His World Wide Web course of study focuses on Data mining software and Multinomial naive bayes.
Bernhard Pfahringer focuses on Artificial intelligence, Machine learning, Data mining, Data stream mining and Data stream. His research integrates issues of Natural language processing and Pattern recognition in his study of Artificial intelligence. His is doing research in Ensemble learning, Decision tree, Semi-supervised learning, Boosting and Support vector machine, both of which are found in Machine learning.
In his study, which falls under the umbrella issue of Data mining, Multi-label classification is strongly linked to Scalability. His Data stream mining research integrates issues from Change detection and Software. His research links Data modeling with Concept drift.
Artificial intelligence, Machine learning, Data stream mining, Concept drift and Data mining are his primary areas of study. His work deals with themes such as Data stream and Natural language processing, which intersect with Artificial intelligence. Machine learning is represented through his Artificial neural network, Semi-supervised learning, Ensemble learning, Decision tree and Feature research.
The study incorporates disciplines such as Classifier, Naive Bayes classifier, Regression, Ensemble forecasting and Data science in addition to Data stream mining. His Concept drift research is multidisciplinary, relying on both Data modeling, Boosting and Feature selection. The various areas that Bernhard Pfahringer examines in his Data mining study include Projection, Cluster analysis, k-nearest neighbors algorithm, Disjoint sets and Dimensionality reduction.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The WEKA data mining software: an update
Mark Hall;Eibe Frank;Geoffrey Holmes;Bernhard Pfahringer.
Sigkdd Explorations (2009)
Classifier chains for multi-label classification
Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank.
Machine Learning (2011)
MOA: Massive Online Analysis, a framework for stream classification and clustering.
Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Philipp Kranen.
Proceedings of the First Workshop on Applications of Pattern Analysis (2010)
MOA: Massive Online Analysis
Albert Bifet;Geoff Holmes;Richard Kirkby;Bernhard Pfahringer.
Journal of Machine Learning Research (2010)
New ensemble methods for evolving data streams
Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Richard Kirkby.
knowledge discovery and data mining (2009)
Weka-A Machine Learning Workbench for Data Mining
Eibe Frank;Mark A. Hall;Geoffrey Holmes;Richard Kirkby.
The Data Mining and Knowledge Discovery Handbook (2009)
Multinomial naive bayes for text categorization revisited
Ashraf M. Kibriya;Eibe Frank;Bernhard Pfahringer;Geoffrey Holmes.
australasian joint conference on artificial intelligence (2004)
Multi-label Classification Using Ensembles of Pruned Sets
J. Read;B. Pfahringer;G. Holmes.
international conference on data mining (2008)
Meta-Learning by Landmarking Various Learning Algorithms
Bernhard Pfahringer;Hilan Bensusan;Christophe G. Giraud-Carrier.
international conference on machine learning (2000)
WEKA---Experiences with a Java Open-Source Project
Remco R. Bouckaert;Eibe Frank;Mark A. Hall;Geoffrey Holmes.
Journal of Machine Learning Research (2010)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: