Bernhard Pfahringer

发表

The WEKA data mining software: an update

Ian H. Witten, Geoff Holmes, Bernhard Pfahringer, 2009, SKDD.

Active Learning with Evolving Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, ECML/PKDD.

Full model selection in the space of data mining operators

Quan Sun, Michael Mayo, Bernhard Pfahringer, 2012, GECCO '12.

Classifier chains for multi-label classification

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2009, Machine Learning.

Multi-label Classification Using Ensembles of Pruned Sets

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Regularisation of neural networks by enforcing Lipschitz continuity pdf

Bernhard Pfahringer, Michael J. Cree, Eibe Frank, 2018, Machine Learning.

MOA: Massive Online Analysis

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, J. Mach. Learn. Res..

Optimizing the Induction of Alternating Decision Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2001, PAKDD.

Random Relational Rules

Bernhard Pfahringer, Grant Anderson, B. Pfahringer, 2006 .

Millions of random rules

Cheng Wang, Geoffrey Holmes, Bernhard Pfahringer, 2004 .

WEKA - Experiences with a Java Open-Source Project

Ian H. Witten, Geoff Holmes, Bernhard Pfahringer, 2010, J. Mach. Learn. Res..

Towards a Framework for Designing Full Model Selection and Optimization Systems

Quan Sun, Michael Mayo, Bernhard Pfahringer, 2013, MCS.

Scalable and efficient multi-label classification for evolving data streams

Geoff Holmes, Jesse Read, Albert Bifet, 2012, Machine Learning.

Adaptive random forests for evolving data stream classification

Talel Abdessalem, Geoff Holmes, Jean Paul Barddal, 2017, Machine Learning.

Correction to: Adaptive random forests for evolving data stream classification

Talel Abdessalem, Geoff Holmes, Jean Paul Barddal, 2019, Machine Learning.

The online performance estimation framework: heterogeneous ensemble learning for data streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2017, Machine Learning.

MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes

Bernhard Pfahringer, Michael J. Cree, Eibe Frank, 2018, ECML/PKDD.

High density-focused uncertainty sampling for active learning over evolving stream data

Dino Ienco, Bernhard Pfahringer, Indre Zliobaite, 2014, BigMine.

Clustering Based Active Learning for Evolving Data Streams

Dino Ienco, Albert Bifet, Bernhard Pfahringer, 2013, Discovery Science.

Active Learning With Drifting Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Weka-A Machine Learning Workbench for Data Mining

Ian H. Witten, Bernhard Pfahringer, Richard Kirkby, 2005, Data Mining and Knowledge Discovery Handbook.

Learning Distance Metrics for Multi-Label Classification

Bernhard Pfahringer, Michael J. Cree, Henry Gouk, 2016, ACML.

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

A. Bifet, B. Pfahringer, Yaqian Zhang, 2022, 2209.13917.

Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents pdf

Jacob Montiel, Bernhard Pfahringer, Vithya Yogarajan, 2021, ArXiv.

Predicting COVID-19 Patient Shielding: A Comprehensive Study pdf

Jacob Montiel, Bernhard Pfahringer, Vithya Yogarajan, 2021, AI.

Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes

Michael Mayo, Bernhard Pfahringer, Henry Gouk, 2020, ACIIDS.

Transformers for Multi-label Classification of Medical Text: An Empirical Comparison

Jacob Montiel, Bernhard Pfahringer, Vithya Yogarajan, 2021, AIME.

Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams

Russel Pears, Albert Bifet, Bernhard Pfahringer, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Locally Weighted Naive Bayes pdf

Bernhard Pfahringer, Eibe Frank, Mark A. Hall, 2002, UAI.

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning

B. Pfahringer, Anton Lee, Yaqian Zhang, 2023, CIKM.

Survey on Online Streaming Continual Learning

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2023, IJCAI.

An empirical analysis of binary transformation strategies and base algorithms for multi-label learning

Adriano Rivolli, Bernhard Pfahringer, Jesse Read, 2020, Machine Learning.

Machine Learning (In) Security: A Stream of Problems

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2020, Digital Threats: Research and Practice.

Clustering for classification

Geoffrey Holmes, Bernhard Pfahringer, Reuben Evans, 2011, 2011 7th International Conference on Information Technology in Asia.

Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction

Bernhard Pfahringer, Felipe Bravo-Marquez, Arun Khanchandani, 2021, Cognitive Computation.

An effective evaluation measure for clustering on evolving data streams

Thomas Seidl, Geoff Holmes, Albert Bifet, 2011, KDD.

Classifier Chains: A Review and Perspectives pdf

Bernhard Pfahringer, Jesse Read, Geoff Holmes, 2019, J. Artif. Intell. Res..

Feature Scoring using Tree-Based Ensembles for Evolving Data Streams

Heitor Murilo Gomes, Rodrigo Fernandes de Mello, Albert Bifet, 2019, 2019 IEEE International Conference on Big Data (Big Data).

Propositionalisation of Multiple Sequence Alignments using Probabilistic Models

Bernhard Pfahringer, Stefan Mutter, Geoffrey Holmes, 2008 .

Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems

Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer, 2018, ECML/PKDD.

Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks

B. Pfahringer, Michael Mayo, Chen Zheng, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

A Radiological Expert System for the PC — Design and Implementation Issues

Bernhard Pfahringer, W. Horn, H. Imhof, 1987 .

Classification Algorithms

Charles X. Ling, T. Shultz, A. Mueen, 2010, Encyclopedia of Machine Learning.