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Journal Articles
- 2024
[j42]Hongyu Wang
, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes:
Feature extractor stacking for cross-domain few-shot learning. Mach. Learn. 113(1): 121-158 (2024)- [j41]Nuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet:
Gradient boosted trees for evolving data streams. Mach. Learn. 113(5): 3325-3352 (2024) - 2023
- [j40]Mi Li, Eibe Frank, Bernhard Pfahringer:
Large scale K-means clustering using GPUs. Data Min. Knowl. Discov. 37(1): 67-109 (2023) - [j39]Jesus Antonanzas, Yunzhe Jia, Eibe Frank, Albert Bifet, Bernhard Pfahringer:
teex: A toolbox for the evaluation of explanations. Neurocomputing 555: 126642 (2023) - [j38]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing. IEEE Trans. Netw. Serv. Manag. 20(3): 3038-3054 (2023) - 2022
- [j37]Felipe Bravo-Marquez, Arun Khanchandani, Bernhard Pfahringer:
Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction. Cogn. Comput. 14(1): 425-441 (2022) - [j36]Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet:
SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams. Data Min. Knowl. Discov. 36(5): 2006-2032 (2022) - [j35]Emanuele Pio Barracchia, Gianvito Pio, Albert Bifet, Heitor Murilo Gomes, Bernhard Pfahringer, Michelangelo Ceci:
LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding. Inf. Sci. 606: 702-721 (2022) - 2021
- [j34]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Improving the performance of bagging ensembles for data streams through mini-batching. Inf. Sci. 580: 260-282 (2021) - [j33]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier Chains: A Review and Perspectives. J. Artif. Intell. Res. 70: 683-718 (2021) - [j32]Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of neural networks by enforcing Lipschitz continuity. Mach. Learn. 110(2): 393-416 (2021) - 2020
- [j31]Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo:
A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric? Appl. Artif. Intell. 34(3): 251-269 (2020) - [j30]Adriano Rivolli, Jesse Read, Carlos Soares, Bernhard Pfahringer, André C. P. L. F. de Carvalho:
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Mach. Learn. 109(8): 1509-1563 (2020) - 2019
- [j29]Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Merit-guided dynamic feature selection filter for data streams. Expert Syst. Appl. 116: 227-242 (2019) - [j28]Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Boosting decision stumps for dynamic feature selection on data streams. Inf. Syst. 83: 13-29 (2019) - [j27]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad:
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets. J. Mach. Learn. Res. 20: 92:1-92:6 (2019) - [j26]Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem:
Correction to: Adaptive random forests for evolving data stream classification. Mach. Learn. 108(10): 1877-1878 (2019) - 2018
- [j25]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach. Learn. 107(1): 149-176 (2018) - [j24]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Transferring sentiment knowledge between words and tweets. Web Intell. 16(4): 203-220 (2018) - 2017
- [j23]Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer:
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. J. Syst. Softw. 127: 278-294 (2017) - [j22]Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem:
Adaptive random forests for evolving data stream classification. Mach. Learn. 106(9-10): 1469-1495 (2017) - 2016
- [j21]Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes:
MEKA: A Multi-label/Multi-target Extension to WEKA. J. Mach. Learn. Res. 17: 21:1-21:5 (2016) - [j20]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Building a Twitter opinion lexicon from automatically-annotated tweets. Knowl. Based Syst. 108: 65-78 (2016) - 2015
- [j19]Luís Torgo, Paula Branco, Rita P. Ribeiro, Bernhard Pfahringer:
Resampling strategies for regression. Expert Syst. J. Knowl. Eng. 32(3): 465-476 (2015) - [j18]Indre Zliobaite, Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes:
Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3): 455-482 (2015) - 2014
- [j17]Andreas Hapfelmeier, Bernhard Pfahringer, Stefan Kramer:
Pruning Incremental Linear Model Trees with Approximate Lookahead. IEEE Trans. Knowl. Data Eng. 26(8): 2072-2076 (2014) - [j16]Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes:
Active Learning With Drifting Streaming Data. IEEE Trans. Neural Networks Learn. Syst. 25(1): 27-39 (2014) - 2013
- [j15]Quan Sun, Bernhard Pfahringer:
Pairwise meta-rules for better meta-learning-based algorithm ranking. Mach. Learn. 93(1): 141-161 (2013) - 2012
- [j14]Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes:
Experiment databases - A new way to share, organize and learn from experiments. Mach. Learn. 87(2): 127-158 (2012) - [j13]Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
Scalable and efficient multi-label classification for evolving data streams. Mach. Learn. 88(1-2): 243-272 (2012) - [j12]Albert Bifet, Eibe Frank, Geoff Holmes, Bernhard Pfahringer:
Ensembles of Restricted Hoeffding Trees. ACM Trans. Intell. Syst. Technol. 3(2): 30:1-30:20 (2012) - 2011
- [j11]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier chains for multi-label classification. Mach. Learn. 85(3): 333-359 (2011) - 2010
- [j10]Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer:
MOA: Massive Online Analysis. J. Mach. Learn. Res. 11: 1601-1604 (2010) - [j9]Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten:
WEKA - Experiences with a Java Open-Source Project. J. Mach. Learn. Res. 11: 2533-2541 (2010) - 2009
- [j8]Mark A. Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten:
The WEKA data mining software: an update. SIGKDD Explor. 11(1): 10-18 (2009) - 2004
- [j7]Hendrik Blockeel, Saso Dzeroski, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer:
Experiments In Predicting Biodegradability. Appl. Artif. Intell. 18(2): 157-181 (2004) - [j6]Bernhard Pfahringer:
The Weka solution to the 2004 KDD Cup. SIGKDD Explor. 6(2): 117-119 (2004) - 2001
- [j5]Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve:
Prediction of Ordinal Classes Using Regression Trees. Fundam. Informaticae 47(1-2): 1-13 (2001) - 2000
- [j4]Klaus Kovar, Johannes Fürnkranz, Johann Petrak, Bernhard Pfahringer, Robert Trappl, Gerhard Widmer:
Searching for Patterns in Political Event Sequences: Experiments with the Keds Database. Cybern. Syst. 31(6): 649-668 (2000) - [j3]Bernhard Pfahringer:
Winning the KDD99 Classification Cup: Bagged Boosting. SIGKDD Explor. 1(2): 65-66 (2000) - 1998
- [j2]Johannes Fürnkranz, Bernhard Pfahringer:
Guest Editorial: First-Order Knowledge Discovery in Databases. Appl. Artif. Intell. 12(5): 345-361 (1998) - 1988
- [j1]Bernhard Pfahringer, M. Hoberstorfer, Robert Trappl:
A decision support system for village health workers in developing countries. Appl. Artif. Intell. 2(1): 47-63 (1988)
Conference and Workshop Papers
- 2024
- [c150]Reginaldo Luna, Guilherme Weigert Cassales, Bernhard Pfahringer, Albert Bifet, Heitor Murilo Gomes, Hermes Senger:
Mini-batching with Fused Training and Testing for Data Streams Processing on the Edge. CF 2024 - [c149]Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer, Albert Bifet, Giacomo Boracchi:
Online Isolation Forest. ICML 2024 - [c148]Yun Sing Koh, Albert Bifet, Karin R. Bryan, Guilherme Weigert Cassales, Olivier Graffeuille, Nick Jin Sean Lim, Phil Mourot, Ding Ning, Bernhard Pfahringer, Varvara Vetrova, Heitor Murilo Gomes:
Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. IJCAI 2024: 7314-7322 - [c147]Nuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet, Yun Sing Koh:
Recurrent Concept Drifts on Data Streams. IJCAI 2024: 8029-8037 - [c146]Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet:
Adaptive Prediction Interval for Data Stream Regression. PAKDD (3) 2024: 130-141 - [c145]Marco Heyden, Heitor Murilo Gomes, Edouard Fouché, Bernhard Pfahringer, Klemens Böhm:
Leveraging Plasticity in Incremental Decision Trees. ECML/PKDD (5) 2024: 38-54 - 2023
- [c144]Anton Lee, Yaqian Zhang, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning. CIKM 2023: 4038-4042 - [c143]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Self-trained Centroid Classifiers for Semi-supervised Cross-domain Few-shot Learning. CoLLAs 2023: 481-492 - [c142]Nuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet:
Survey on Online Streaming Continual Learning. IJCAI 2023: 6628-6637 - 2022
- [c141]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Predicting COVID-19 Patient Shielding: A Comprehensive Study. AI 2022: 332-343 - [c140]Rajchada Chanajitt, Bernhard Pfahringer, Heitor Murilo Gomes, Vithya Yogarajan:
Multiclass Malware Classification Using Either Static Opcodes or Dynamic API Calls. AI 2022: 427-441 - [c139]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Better Self-training for Image Classification Through Self-supervision. AI 2022: 645-657 - [c138]Nuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Adaptive Neural Networks for Online Domain Incremental Continual Learning. DS 2022: 89-103 - [c137]Hongyu Wang, Huon Fraser, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes:
Experiments in Cross-domain Few-shot Learning for Image Classification: Extended Abstract. Meta-Knowledge Transfer @ ECML/PKDD 2022: 81-83 - [c136]Vithya Yogarajan, Bernhard Pfahringer, Tony Smith, Jacob Montiel:
Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels. ICANN (2) 2022: 209-221 - [c135]Rajchada Chanajitt, Bernhard Pfahringer, Heitor Murilo Gomes:
A Comparison of Neural Network Architectures for Malware Classification Based on Noriben Operation Sequences. ICANN (1) 2022: 428-440 - [c134]Nuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Adaptive Online Domain Incremental Continual Learning. ICANN (1) 2022: 491-502 - [c133]Nuwan Gunasekara, Heitor Murilo Gomes, Bernhard Pfahringer, Albert Bifet:
Online Hyperparameter Optimization for Streaming Neural Networks. IJCNN 2022: 1-9 - [c132]Chen Zheng, Bernhard Pfahringer, Michael Mayo:
Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks. IJCNN 2022: 1-8 - [c131]Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia:
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal. NeurIPS 2022 - 2021
- [c130]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Transformers for Multi-label Classification of Medical Text: An Empirical Comparison. AIME 2021: 114-123 - [c129]Rajchada Chanajitt, Bernhard Pfahringer, Heitor Murilo Gomes:
Combining Static and Dynamic Analysis to Improve Machine Learning-based Malware Classification. DSAA 2021: 1-10 - [c128]Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer:
PolyLM: Learning about Polysemy through Language Modeling. EACL 2021: 563-574 - [c127]Saulo Martiello Mastelini, Jacob Montiel, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, André C. P. L. F. de Carvalho:
Fast and lightweight binary and multi-branch Hoeffding Tree Regressors. ICDM (Workshops) 2021: 380-388 - [c126]Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, Nick Jin Sean Lim:
Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality. ECML/PKDD (2) 2021: 699-714 - 2020
- [c125]Vithya Yogarajan, Henry Gouk, Tony Smith, Michael Mayo, Bernhard Pfahringer:
Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes. ACIIDS (1) 2020: 97-108 - [c124]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Transfer of Pretrained Model Weights Substantially Improves Semi-supervised Image Classification. Australasian Conference on Artificial Intelligence 2020: 433-444 - [c123]Hongyu Wang, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo:
A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning. Australasian Conference on Artificial Intelligence 2020: 445-457 - [c122]Alessio Bernardo, Heitor Murilo Gomes, Jacob Montiel, Bernhard Pfahringer, Albert Bifet, Emanuele Della Valle:
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams. IEEE BigData 2020: 483-492 - [c121]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching. HPCC/DSS/SmartCity 2020: 138-146 - [c120]Maroua Bahri, Bernhard Pfahringer, Albert Bifet, Silviu Maniu:
Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams. IDA 2020: 40-53 - [c119]Heitor Murilo Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, Albert Bifet:
On Ensemble Techniques for Data Stream Regression. IJCNN 2020: 1-8 - [c118]Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet:
Adaptive XGBoost for Evolving Data Streams. IJCNN 2020: 1-8 - [c117]Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer:
confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. LION 2020: 80-95 - 2019
- [c116]Alex Yuxuan Peng, Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer:
Investigating the effect of novel classes in semi-supervised learning. ACML 2019: 615-630 - [c115]Henry Gouk, Bernhard Pfahringer, Eibe Frank:
Stochastic Gradient Trees. ACML 2019: 1094-1109 - [c114]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Semi-supervised Learning Using Siamese Networks. Australasian Conference on Artificial Intelligence 2019: 586-597 - [c113]Heitor Murilo Gomes, Rodrigo Fernandes de Mello, Bernhard Pfahringer, Albert Bifet:
Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. IEEE BigData 2019: 761-769 - [c112]Jörg Wicker, Yan Cathy Hua, Rayner Rebello, Bernhard Pfahringer:
XOR-Based Boolean Matrix Decomposition. ICDM 2019: 638-647 - [c111]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
On Calibration of Nested Dichotomies. PAKDD (1) 2019: 69-80 - [c110]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Ensembles of Nested Dichotomies with Multiple Subset Evaluation. PAKDD (1) 2019: 81-93 - [c109]Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer:
Towards Automated Configuration of Stream Clustering Algorithms. PKDD/ECML Workshops (1) 2019: 137-143 - [c108]Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer:
An ELMo-inspired approach to SemDeep-5's Word-in-Context task. SemDeep@IJCAI 2019: 21-25 - 2018
- [c107]Bartosz Krawczyk, Bernhard Pfahringer, Michal Wozniak:
Combining active learning with concept drift detection for data stream mining. IEEE BigData 2018: 2239-2244 - [c106]Edmond Zhang, Reece Robinson, Bernhard Pfahringer:
Deep Holistic Representation Learning from EHR. ISMICT 2018: 1-6 - [c105]Alex Yuxuan Peng, Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer:
Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems. ECML/PKDD (1) 2018: 410-425 - [c104]Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree:
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes. ECML/PKDD (1) 2018: 541-556 - [c103]Lanqin Yuan, Bernhard Pfahringer, Jean Paul Barddal:
Iterative subset selection for feature drifting data streams. SAC 2018: 510-517 - 2017
- [c102]Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Probability Calibration Trees. ACML 2017: 145-160 - [c101]Min-Hsien Weng, Bernhard Pfahringer, Mark Utting:
Static techniques for reducing memory usage in the C implementation of whiley programs. ACSW 2017: 15:1-15:8 - [c100]Paula Branco, Luís Torgo, Rita P. Ribeiro, Eibe Frank, Bernhard Pfahringer, Markus Michael Rau:
Learning Through Utility Optimization in Regression Tasks. DSAA 2017: 30-39 - [c99]Vítor Cerqueira, Luís Torgo, Mariana Oliveira, Bernhard Pfahringer:
Dynamic and Heterogeneous Ensembles for Time Series Forecasting. DSAA 2017: 242-251 - [c98]Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer:
Extremely Fast Decision Tree Mining for Evolving Data Streams. KDD 2017: 1733-1742 - 2016
- [c97]Henry Gouk, Bernhard Pfahringer, Michael J. Cree:
Learning Distance Metrics for Multi-Label Classification. ACML 2016: 318-333 - [c96]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis. ECAI 2016: 498-506 - [c95]Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer, Albert Bifet:
On Dynamic Feature Weighting for Feature Drifting Data Streams. ECML/PKDD (2) 2016: 129-144 - [c94]Tim Leathart, Bernhard Pfahringer, Eibe Frank:
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection. ECML/PKDD (2) 2016: 179-194 - [c93]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach. WI 2016: 145-152 - [c92]Felipe Bravo-Marquez, Eibe Frank, Saif M. Mohammad, Bernhard Pfahringer:
Determining Word-Emotion Associations from Tweets by Multi-label Classification. WI 2016: 536-539 - 2015
- [c91]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. ICDM 2015: 1003-1008 - [c90]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets. IJCAI 2015: 1229-1235 - [c89]Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer:
Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams. IJCNN 2015: 1-8 - [c88]Albert Bifet, Gianmarco De Francisci Morales, Jesse Read, Geoff Holmes, Bernhard Pfahringer:
Efficient Online Evaluation of Big Data Stream Classifiers. KDD 2015: 59-68 - [c87]Bernhard Pfahringer:
On a Few Recent Developments in Meta-Learning for Algorithm Ranking and Selection. MetaSel@PKDD/ECML 2015: 2 - [c86]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
From Unlabelled Tweets to Twitter-specific Opinion Words. SIGIR 2015: 743-746 - [c85]Min-Hsien Weng, Mark Utting, Bernhard Pfahringer:
Bound Analysis for Whiley Programs. USE@FM 2015: 53-67 - 2014
- [c84]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Algorithm Selection on Data Streams. Discovery Science 2014: 325-336 - [c83]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Towards Meta-learning over Data Streams. MetaSel@ECAI 2014: 37-38 - [c82]Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet:
Détection de changements dans des flots de données qualitatives. EGC 2014: 517-520 - [c81]Dino Ienco, Indre Zliobaite, Bernhard Pfahringer:
High density-focused uncertainty sampling for active learning over evolving stream data. BigMine 2014: 133-148 - [c80]Quan Sun, Bernhard Pfahringer:
Hierarchical Meta-Rules for Scalable Meta-Learning. PRICAI 2014: 383-395 - [c79]Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet:
Change detection in categorical evolving data streams. SAC 2014: 792-797 - 2013
- [c78]Bernhard Pfahringer:
The MOA Data Stream Mining Tool: A Mid-Term Report. MLSDA@AUS-AI 2013: 3 - [c77]Eibe Frank, Bernhard Pfahringer:
Propositionalisation of Multi-instance Data Using Random Forests. Australasian Conference on Artificial Intelligence 2013: 362-373 - [c76]Dino Ienco, Albert Bifet, Indre Zliobaite, Bernhard Pfahringer:
Clustering Based Active Learning for Evolving Data Streams. Discovery Science 2013: 79-93 - [c75]Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, Paula Branco:
SMOTE for Regression. EPIA 2013: 378-389 - [c74]Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite:
CD-MOA: Change Detection Framework for Massive Online Analysis. IDA 2013: 92-103 - [c73]Samuel Sarjant, Bernhard Pfahringer, Kurt Driessens, Tony Smith:
A Direct Policy-Search Algorithm for Relational Reinforcement Learning. ILP 2013: 76-92 - [c72]Quan Sun, Bernhard Pfahringer, Michael Mayo:
Towards a Framework for Designing Full Model Selection and Optimization Systems. MCS 2013: 259-270 - [c71]Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes:
Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. ECML/PKDD (1) 2013: 465-479 - [c70]Madeleine Seeland, Stefan Kramer, Bernhard Pfahringer:
Model selection based product kernel learning for regression on graphs. SAC 2013: 136-143 - [c69]Albert Bifet, Bernhard Pfahringer, Jesse Read, Geoff Holmes:
Efficient data stream classification via probabilistic adaptive windows. SAC 2013: 801-806 - 2012
- [c68]Quan Sun, Bernhard Pfahringer:
Bagging Ensemble Selection for Regression. Australasian Conference on Artificial Intelligence 2012: 695-706 - [c67]Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read:
Stream Data Mining Using the MOA Framework. DASFAA (2) 2012: 309-313 - [c66]Quan Sun, Bernhard Pfahringer, Michael Mayo:
Full model selection in the space of data mining operators. GECCO (Companion) 2012: 1503-1504 - [c65]Jesse Read, Albert Bifet, Bernhard Pfahringer, Geoff Holmes:
Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data. IDA 2012: 313-323 - [c64]Madeleine Seeland, Fabian Buchwald, Stefan Kramer, Bernhard Pfahringer:
Maximum Common Subgraph based locally weighted regression. SAC 2012: 165-172 - [c63]Jörg Wicker, Bernhard Pfahringer, Stefan Kramer:
Multi-label classification using boolean matrix decomposition. SAC 2012: 179-186 - 2011
- [c62]Bernhard Pfahringer:
Semi-random Model Tree Ensembles: An Effective and Scalable Regression Method. Australasian Conference on Artificial Intelligence 2011: 231-240 - [c61]Quan Sun, Bernhard Pfahringer:
Bagging Ensemble Selection. Australasian Conference on Artificial Intelligence 2011: 251-260 - [c60]Samuel Sarjant, Bernhard Pfahringer, Kurt Driessens, Tony Smith:
Using the online cross-entropy method to learn relational policies for playing different games. CIG 2011: 182-189 - [c59]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer:
MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data. Discovery Science 2011: 46-60 - [c58]Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Ricard Gavaldà:
Mining frequent closed graphs on evolving data streams. KDD 2011: 591-599 - [c57]Hardy Kremer, Philipp Kranen, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
An effective evaluation measure for clustering on evolving data streams. KDD 2011: 868-876 - [c56]Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoff Holmes:
Active Learning with Evolving Streaming Data. ECML/PKDD (3) 2011: 597-612 - [c55]Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl:
MOA: A Real-Time Analytics Open Source Framework. ECML/PKDD (3) 2011: 617-620 - [c54]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Ricard Gavaldà:
Detecting Sentiment Change in Twitter Streaming Data. WAPA 2011: 5-11 - [c53]Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
Streaming Multi-label Classification. WAPA 2011: 19-25 - [c52]Indre Zliobaite, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
MOA Concept Drift Active Learning Strategies for Streaming Data. WAPA 2011: 48-55 - 2010
- [c51]Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA. ICDM Workshops 2010: 1400-1403 - [c50]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Eibe Frank:
Fast Perceptron Decision Tree Learning from Evolving Data Streams. PAKDD (2) 2010: 299-310 - [c49]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer:
Leveraging Bagging for Evolving Data Streams. ECML/PKDD (1) 2010: 135-150 - [c48]Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl:
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. WAPA 2010: 44-50 - [c47]Albert Bifet, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking. ACML 2010: 225-240 - 2009
- [c46]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Ricard Gavaldà:
Improving Adaptive Bagging Methods for Evolving Data Streams. ACML 2009: 23-37 - [c45]Stefan Mutter, Bernhard Pfahringer, Geoffrey Holmes:
The Positive Effects of Negative Information: Extending One-Class Classification Models in Binary Proteomic Sequence Classification. Australasian Conference on Artificial Intelligence 2009: 260-269 - [c44]Grant Anderson, Bernhard Pfahringer:
Relational Random Forests Based on Random Relational Rules. IJCAI 2009: 986-991 - [c43]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Ricard Gavaldà:
New ensemble methods for evolving data streams. KDD 2009: 139-148 - [c42]Jesse Read, Bernhard Pfahringer, Geoffrey Holmes, Eibe Frank:
Classifier Chains for Multi-label Classification. ECML/PKDD (2) 2009: 254-269 - 2008
- [c41]Stefan Mutter, Bernhard Pfahringer, Geoffrey Holmes:
Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis. Australasian Conference on Artificial Intelligence 2008: 278-288 - [c40]Xing Wu, Geoffrey Holmes, Bernhard Pfahringer:
Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search. Australasian Conference on Artificial Intelligence 2008: 355-361 - [c39]Jesse Read, Bernhard Pfahringer, Geoffrey Holmes:
Multi-label Classification Using Ensembles of Pruned Sets. ICDM 2008: 995-1000 - [c38]Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes:
Organizing the World's Machine Learning Information. ISoLA 2008: 693-708 - [c37]Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby:
Handling Numeric Attributes in Hoeffding Trees. PAKDD 2008: 296-307 - [c36]Grant Anderson, Bernhard Pfahringer:
Exploiting Propositionalization Based on Random Relational Rules for Semi-supervised Learning. PAKDD 2008: 494-502 - [c35]Joaquin Vanschoren, Bernhard Pfahringer, Geoffrey Holmes:
Learning from the Past with Experiment Databases. PRICAI 2008: 485-496 - 2007
- [c34]Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby:
New Options for Hoeffding Trees. Australian Conference on Artificial Intelligence 2007: 90-99 - [c33]Grant Anderson, Bernhard Pfahringer:
Clustering Relational Data Based on Randomized Propositionalization. ILP 2007: 39-48 - [c32]Bernhard Pfahringer, Claire Leschi, Peter Reutemann:
Scaling Up Semi-supervised Learning: An Efficient and Effective LLGC Variant. PAKDD 2007: 236-247 - 2006
- [c31]Kurt Driessens, Peter Reutemann, Bernhard Pfahringer, Claire Leschi:
Using Weighted Nearest Neighbor to Benefit from Unlabeled Data. PAKDD 2006: 60-69 - [c30]Eibe Frank, Bernhard Pfahringer:
Improving on Bagging with Input Smearing. PAKDD 2006: 97-106 - 2005
- [c29]Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby:
Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams. CITA 2005: 130-36 - [c28]Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer:
Stress-Testing Hoeffding Trees. PKDD 2005: 495-502 - 2004
- [c27]Mi Li, Geoffrey Holmes, Bernhard Pfahringer:
Clustering Large Datasets Using Cobweb and K-Means in Tandem. Australian Conference on Artificial Intelligence 2004: 368-379 - [c26]Ashraf M. Kibriya, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Multinomial Naive Bayes for Text Categorization Revisited. Australian Conference on Artificial Intelligence 2004: 488-499 - [c25]Peter Reutemann, Bernhard Pfahringer, Eibe Frank:
A Toolbox for Learning from Relational Data with Propositional and Multi-instance Learners. Australian Conference on Artificial Intelligence 2004: 1017-1023 - 2003
- [c24]Nils Weidmann, Eibe Frank, Bernhard Pfahringer:
A Two-Level Learning Method for Generalized Multi-instance Problems. ECML 2003: 468-479 - [c23]Maximilien Sauban, Bernhard Pfahringer:
Text Categorisation Using Document Profiling. PKDD 2003: 411-422 - [c22]Eibe Frank, Mark A. Hall, Bernhard Pfahringer:
Locally Weighted Naive Bayes. UAI 2003: 249-256 - 2002
- [c21]Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark A. Hall:
Multiclass Alternating Decision Trees. ECML 2002: 161-172 - [c20]Roger Clayton, John G. Cleary, Bernhard Pfahringer, Mark Utting:
Tabling Structures for Bottom-Up Logic Programming. LOPSTR 2002: 50-51 - 2001
- [c19]Bernhard Pfahringer, Geoffrey Holmes, Gabi Schmidberger:
Wrapping Boosters against Noise. Australian Joint Conference on Artificial Intelligence 2001: 402-413 - [c18]Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby:
Optimizing the Induction of Alternating Decision Trees. PAKDD 2001: 477-487 - 2000
- [c17]Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer:
Learning to Use Operational Advice. ECAI 2000: 291-295 - [c16]Bernhard Pfahringer, Hilan Bensusan, Christophe G. Giraud-Carrier:
Meta-Learning by Landmarking Various Learning Algorithms. ICML 2000: 743-750 - [c15]Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve:
Prediction of Ordinal Classes Using Regression Trees. ISMIS 2000: 426-434 - 1999
- [c14]Saso Dzeroski, Hendrik Blockeel, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer:
Experiments in Predicting Biodegradability. ILP 1999: 80-91 - 1998
- [c13]Stefan Kramer, Bernhard Pfahringer, Christoph Helma:
Stochastic Propositionalization of Non-determinate Background Knowledge. ILP 1998: 80-94 - 1997
- [c12]Bernhard Pfahringer:
Compression-Based Pruning of Decision Lists. ECML 1997: 199-212 - [c11]Stefan Kramer, Bernhard Pfahringer, Christoph Helma:
Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail. KDD 1997: 223-226 - 1996
- [c10]Stefan Kramer, Bernhard Pfahringer:
Efficient Search for Strong Partial Determinations. KDD 1996: 371-374 - 1995
- [c9]Bernhard Pfahringer:
A New MDL Measure for Robust Rule Induction (Extended Abstract). ECML 1995: 331-334 - [c8]Bernhard Pfahringer:
Compression-Based Discretization of Continuous Attributes. ICML 1995: 456-463 - [c7]Bernhard Pfahringer, Stefan Kramer:
Compression-Based Evaluation of Partial Determinations. KDD 1995: 234-239 - 1994
- [c6]Bernhard Pfahringer:
Controlling Constructive Induction in CIPF: An MDL Approach. ECML 1994: 242-256 - [c5]Bernhard Pfahringer:
Robust Constructive Induction. KI 1994: 118-129 - 1992
- [c4]Bernhard Pfahringer:
The Logical Way to Build a DL-based KR System. Description Logics 1992: 76-77 - 1991
- [c3]Ernst Buchberger, Elizabeth Garner, Wolfgang Heinz, Johannes Matiasek, Bernhard Pfahringer:
VIE-DU: Dialogue by Unification. ÖGAI 1991: 42-51 - 1989
- [c2]Bernhard Pfahringer:
Extending Explanation-Based Generalization. ÖGAI 1989: 149-153 - 1985
- [c1]Bernhard Pfahringer, Christian Holzbaur:
VIE-KET: Frames + Prolog. ÖGAI 1985: 132-139
Parts in Books or Collections
- 2010
- [p2]Eibe Frank, Mark A. Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H. Witten, Len Trigg:
Weka-A Machine Learning Workbench for Data Mining. Data Mining and Knowledge Discovery Handbook 2010: 1269-1277 - 2005
- [p1]Eibe Frank, Mark A. Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer:
WEKA - A Machine Learning Workbench for Data Mining. The Data Mining and Knowledge Discovery Handbook 2005: 1305-1314
Editorship
- 2015
- [e3]Bernhard Pfahringer, Jochen Renz:
AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Canberra, ACT, Australia, November 30 - December 4, 2015, Proceedings. Lecture Notes in Computer Science 9457, Springer 2015, ISBN 978-3-319-26349-6 [contents] - 2010
- [e2]Bernhard Pfahringer, Geoffrey Holmes, Achim G. Hoffmann:
Discovery Science - 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010. Proceedings. Lecture Notes in Computer Science 6332, Springer 2010, ISBN 978-3-642-16183-4 [contents] - 2005
- [e1]Stefan Kramer, Bernhard Pfahringer:
Inductive Logic Programming, 15th International Conference, ILP 2005, Bonn, Germany, August 10-13, 2005, Proceedings. Lecture Notes in Computer Science 3625, Springer 2005, ISBN 3-540-28177-0 [contents]
Reference Works
- 2017
- [r3]Bernhard Pfahringer:
Conjunctive Normal Form. Encyclopedia of Machine Learning and Data Mining 2017: 260-261 - [r2]Bernhard Pfahringer:
Disjunctive Normal Form. Encyclopedia of Machine Learning and Data Mining 2017: 371-372 - 2010
- [r1]Bernhard Pfahringer:
Conjunctive Normal Form. Encyclopedia of Machine Learning 2010: 209-210
Informal and Other Publications
- 2024
- [i28]Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer, Albert Bifet:
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis. CoRR abs/2408.16187 (2024) - 2023
- [i27]Anton Lee, Yaqian Zhang, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning. CoRR abs/2310.20052 (2023) - 2022
- [i26]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing. CoRR abs/2201.06205 (2022) - [i25]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoffrey Holmes:
Cross-domain Few-shot Meta-learning Using Stacking. CoRR abs/2205.05831 (2022) - [i24]Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia:
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal. CoRR abs/2209.13917 (2022) - 2021
- [i23]Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer:
PolyLM: Learning about Polysemy through Language Modeling. CoRR abs/2101.10448 (2021) - [i22]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Better Self-training for Image Classification through Self-supervision. CoRR abs/2109.00778 (2021) - [i21]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification. CoRR abs/2109.00788 (2021) - [i20]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Semi-Supervised Learning using Siamese Networks. CoRR abs/2109.00794 (2021) - [i19]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Predicting COVID-19 Patient Shielding: A Comprehensive Study. CoRR abs/2110.00183 (2021) - [i18]Vithya Yogarajan, Bernhard Pfahringer, Tony Smith, Jacob Montiel:
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents. CoRR abs/2112.01718 (2021) - [i17]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Improving the performance of bagging ensembles for data streams through mini-batching. CoRR abs/2112.09834 (2021) - 2020
- [i16]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes. CoRR abs/2004.00430 (2020) - [i15]Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet:
Adaptive XGBoost for Evolving Data Streams. CoRR abs/2005.07353 (2020) - [i14]Fabricio Ceschin, Heitor Murilo Gomes, Marcus Botacin, Albert Bifet, Bernhard Pfahringer, Luiz S. Oliveira, André Grégio:
Machine Learning (In) Security: A Stream of Problems. CoRR abs/2010.16045 (2020) - 2019
- [i13]Henry Gouk, Bernhard Pfahringer, Eibe Frank:
Stochastic Gradient Trees. CoRR abs/1901.07777 (2019) - [i12]Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo:
Automatic end-to-end De-identification: Is high accuracy the only metric? CoRR abs/1901.10583 (2019) - [i11]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier Chains: A Review and Perspectives. CoRR abs/1912.13405 (2019) - 2018
- [i10]Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of Neural Networks by Enforcing Lipschitz Continuity. CoRR abs/1804.04368 (2018) - [i9]Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree:
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes. CoRR abs/1804.05965 (2018) - [i8]Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Probability Calibration Trees. CoRR abs/1808.00111 (2018) - [i7]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Ensembles of Nested Dichotomies with Multiple Subset Evaluation. CoRR abs/1809.02740 (2018) - [i6]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
On the Calibration of Nested Dichotomies for Large Multiclass Tasks. CoRR abs/1809.02744 (2018) - [i5]Vithya Yogarajan, Michael Mayo, Bernhard Pfahringer:
A survey of automatic de-identification of longitudinal clinical narratives. CoRR abs/1810.06765 (2018) - 2016
- [i4]Tim Leathart, Bernhard Pfahringer, Eibe Frank:
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection. CoRR abs/1604.01854 (2016) - 2015
- [i3]Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer:
Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams. CoRR abs/1504.06366 (2015) - [i2]Henry Gouk, Bernhard Pfahringer, Michael J. Cree:
Learning Similarity Metrics by Factorising Adjacency Matrices. CoRR abs/1511.06442 (2015) - 2012
- [i1]Eibe Frank, Mark A. Hall, Bernhard Pfahringer:
Locally Weighted Naive Bayes. CoRR abs/1212.2487 (2012)
Coauthor Index
[j41] [j39] [j38] [j36] [j35] [j34] [j29] [j28] [j26] [j22] [j18] [j16] [j13] [j12] [j10] [c150] [c149] [c148] [c147] [c146] [c144] [c142] [c138] [c134] [c133] [c131] [c127] [c126] [c122] [c121] [c120] [c119] [c118] [c117] [c113] [c109] [c98] [c95] [c89] [c88] [c82] [c79] [c76] [c74] [c71] [c69] [c67] [c65] [c59] [c58] [c57] [c56] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c48] [c47] [c46] [c43] [i28] [i27] [i26] [i24] [i17] [i15] [i14] [i3]
[j42] [j40] [j39] [j33] [j32] [j27] [j24] [j20] [j12] [j11] [j9] [j8] [c143] [c139] [c137] [c131] [c126] [c124] [c123] [c118] [c115] [c114] [c111] [c110] [c104] [c102] [c100] [c96] [c94] [c93] [c92] [c90] [c86] [c77] [c50] [c47] [c42] [c30] [c26] [c25] [c24] [c22] [c21] [p2] [p1] [i25] [i24] [i22] [i21] [i20] [i15] [i13] [i11] [i10] [i9] [i8] [i7] [i6] [i4] [i1]
[j42] [j33] [j26] [j25] [j22] [j21] [j18] [j16] [j14] [j13] [j12] [j11] [j10] [j9] [j8] [c143] [c137] [c111] [c110] [c102] [c98] [c91] [c88] [c84] [c83] [c74] [c71] [c69] [c67] [c65] [c59] [c58] [c57] [c56] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c48] [c47] [c46] [c45] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c35] [c34] [c29] [c28] [c27] [c26] [c21] [c19] [c18] [p2] [p1] [e2] [i25] [i11] [i8] [i7] [i6]
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