G. Krempl

发表

Optimised probabilistic active learning ( OPAL ) For fast , non-myopic , cost-sensitive active classification

V. Lemaire, G. Krempl, D. Kottke, 2015 .

Artificial Intelligence Keeping up with Fraud An Active Learning Approach for Imbalanced Non-Stationary Data Streams

G. Krempl, M. D. Wit, 2017 .

Digitized Locksmith Forensics: Design and Implementation of a Computer-Aided Forensic Analysis

Christian Hansen, V. Köppen, E. Schallehn, 2014 .

Stream-based active learning for sliding windows under the influence of verification latency

B. Sick, G. Krempl, D. Kottke, 2021, Machine Learning.

Real-World Challenges for Data Stream Mining

I. Žliobaitė, George Forman, G. Krempl, 2013 .

Workshop on Interactive Adaptive Learning

R. Polikar, V. Lemaire, B. Sick, 2018 .

Classification in Presence of Drift and Latency

Georg Krempl, Vera Hofer, V. Hofer, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

The Algorithm APT to Classify in Concurrence of Latency and Drift

Georg Krempl, G. Krempl, 2011, IDA.

Temporal density extrapolation using a dynamic basis approach

Georg Krempl, Vera Hofer, Dominik Lang, 2019, Data Mining and Knowledge Discovery.

Toward optimal probabilistic active learning using a Bayesian approach pdf

Bernhard Sick, Georg Krempl, Daniel Kottke, 2020, Machine Learning.

Constructing and predicting school advice for academic achievement: a comparison of item response theory and machine learning techniques

Georg Krempl, Jesse Koops, Matthieu J. S. Brinkhuis, 2020, LAK.

Beyond Adaptation: Understanding Distributional Changes

Geoffrey I. Webb, E. Hüllermeier, V. Hofer, 2021 .

Advances in Intelligent Data Analysis XVIII: 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings

Michael R. Berthold, Georg Krempl, Ad Feelders, 2020, IDA.

Open challenges for data stream mining research

Eyke Hüllermeier, Myra Spiliopoulou, Mark Last, 2014, SKDD.

A hierarchical tree layout algorithm with an application to corporate management in a change process

Georg Krempl, Vera Hofer, V. Hofer, 2012, Expert Syst. Appl..

Challenges of Reliable, Realistic and Comparable Active Learning Evaluation

Bernhard Sick, Adrian Calma, Georg Krempl, 2017, IAL@PKDD/ECML.

Correcting the Usage of the Hoeffding Inequality in Stream Mining

Myra Spiliopoulou, Georg Krempl, Pawel Matuszyk, 2013, IDA.

Temporal density extrapolation using a dynamic basis approach

V. Hofer, G. Krempl, D. Lang, 2019, Data Mining and Knowledge Discovery.

Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372)

Geoffrey I. Webb, E. Hüllermeier, V. Hofer, 2020, Dagstuhl Reports.

Drift mining in data: A framework for addressing drift in classification

Georg Krempl, Vera Hofer, V. Hofer, 2013, Comput. Stat. Data Anal..

Clustering-Based Optimised Probabilistic Active Learning (COPAL)

Myra Spiliopoulou, Georg Krempl, Tuan Cuong Ha, 2015, Discovery Science.

Workshop on Active Learning : Applications , Foundations and Emerging Trends

V. Lemaire, G. Krempl, Edwin Lughofer Daniel Kottke, 2016 .

Probabilistic Active Learning for Active Class Selection pdf

Myra Spiliopoulou, Bernhard Sick, Tim Sabsch, 2021, ArXiv.

How to select information that matters: a comparative study on active learning strategies for classification

Vincent Lemaire, Georg Krempl, Christian Beyer, 2015, I-KNOW.

Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency

Myra Spiliopoulou, Georg Krempl, Daniel Kottke, 2014, Discovery Science.

Probabilistic Active Learning: A Short Proposition

Myra Spiliopoulou, Georg Krempl, Daniel Kottke, 2014, ECAI.

Statistical Analysis of Pairwise Connectivity

Georg Krempl, Daniel Kottke, Tuan Pham, 2021, IFIP Working Conference on Database Semantics.

ACE: A Novel Approach for the Statistical Analysis of Pairwise Connectivity pdf

Georg Krempl, Daniel Kottke, Tuan Pham Minh, 2021, ArXiv.

A Stopping Criterion for Transductive Active Learning

B. Sick, G. Krempl, D. Kottke, 2022, ECML/PKDD.