What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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.
His most cited work include:
- The WEKA data mining software: an update (15684 citations)
- Classifier chains for multi-label classification (1233 citations)
- MOA: Massive Online Analysis (1030 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (58.52%)
- Machine learning (47.60%)
- Data mining (27.95%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (58.52%)
- Machine learning (47.60%)
- Data stream mining (27.07%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Regularisation of Neural Networks by Enforcing Lipschitz Continuity (101 citations)
- Machine Learning for Data Streams: With Practical Examples in Moa (89 citations)
- The online performance estimation framework: heterogeneous ensemble learning for data streams (46 citations)
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.
