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ORCIDTobias Scheffer
Person information
- affiliation: University of Potsdam, Germany
- affiliation (former): Max Planck Institute for Informatics, Saarbrücken, Germany
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2020 – today
- 2024
[j25]Paul Prasse
, David R. Reich, Silvia Makowski, Tobias Scheffer, Lena A. Jäger:
Improving cognitive-state analysis from eye gaze with synthetic eye-movement data. Comput. Graph. 119: 103901 (2024)- [c88]Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer, Lena A. Jäger:
Fine-Tuning Pre-Trained Language Models with Gaze Supervision. ACL (Short Papers) 2024: 217-224 - [c87]Pedro Alonso Campana, Paul Prasse, Tobias Scheffer:
Predicting Dose-Response Curves with Deep Neural Networks. ICML 2024 - 2023
- [j24]Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer, Lena A. Jäger:
Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading. Proc. ACM Hum. Comput. Interact. 7(ETRA): 1-24 (2023) - [c86]Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer, Lena A. Jäger:
Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding. EMNLP 2023: 6500-6507 - [c85]Daniel G. Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger:
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. ETRA 2023: 3:1-3:8 - [c84]Paul Prasse, David R. Reich, Silvia Makowski, Seoyoung Ahn, Tobias Scheffer, Lena A. Jäger:
SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks. ETRA 2023: 18:1-18:9 - [c83]Silvia Makowski, Paul Prasse, Lena Ann Jäger, Tobias Scheffer:
Detection of Drowsiness and Impending Microsleep from Eye Movements. Gaze Meets ML 2023: 142-160 - [c82]Silvia Makowski, Annika Bätz, Paul Prasse, Lena A. Jäger, Tobias Scheffer:
Detection of Alcohol Inebriation from Eye Movements. KES 2023: 2086-2095 - [i13]Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer, Lena A. Jäger:
Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading. CoRR abs/2304.10784 (2023) - [i12]Daniel G. Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger:
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. CoRR abs/2304.13536 (2023) - [i11]Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer, Lena A. Jäger:
Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding. CoRR abs/2310.14676 (2023) - 2022
- [j23]Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, Isabel Dziobek:
Author Correction: Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj Digit. Medicine 5 (2022) - [c81]Paul Prasse, David R. Reich, Silvia Makowski, Lena A. Jäger, Tobias Scheffer:
Fairness in Oculomotoric Biometric Identification. ETRA 2022: 22:1-22:8 - [c80]Daniel Krakowczyk, David R. Reich, Paul Prasse, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger:
Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification. Gaze Meets ML 2022: 66-97 - [c79]Silvia Makowski, Paul Prasse, Lena A. Jäger, Tobias Scheffer:
Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue. IJCB 2022: 1-9 - [c78]Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, Lena A. Jäger:
Detection of ADHD Based on Eye Movements During Natural Viewing. ECML/PKDD (6) 2022: 403-418 - [i10]Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, Lena A. Jäger:
Detection of ADHD based on Eye Movements during Natural Viewing. CoRR abs/2207.01377 (2022) - 2021
- [j22]Silvia Makowski, Paul Prasse, David R. Reich, Daniel Krakowczyk, Lena A. Jäger, Tobias Scheffer:
DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks. IEEE Trans. Biom. Behav. Identity Sci. 3(4): 506-518 (2021) - [c77]Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp, Lukás Bajer, Tobias Scheffer:
Learning Explainable Representations of Malware Behavior. ECML/PKDD (4) 2021: 53-68 - [i9]Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp, Lukás Bajer, Tobias Scheffer:
Learning Explainable Representations of Malware Behavior. CoRR abs/2106.12328 (2021) - 2020
- [j21]Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, Isabel Dziobek:
Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj Digit. Medicine 3 (2020) - [c76]Silvia Makowski, Lena A. Jäger, Paul Prasse, Tobias Scheffer:
Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes. IJCB 2020: 1-10 - [c75]Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, Tobias Scheffer:
Discriminative Viewer Identification using Generative Models of Eye Gaze. KES 2020: 1348-1357 - [c74]Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil, Tobias Scheffer:
On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. KES 2020: 2088-2097 - [i8]Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, Tobias Scheffer:
Discriminative Viewer Identification using Generative Models of Eye Gaze. CoRR abs/2003.11399 (2020)
2010 – 2019
- 2019
- [j20]Paul Prasse, René Knaebel, Lukás Machlica, Tomás Pevný, Tobias Scheffer:
Joint detection of malicious domains and infected clients. Mach. Learn. 108(8-9): 1353-1368 (2019) - [c73]Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer:
Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye. ECML/PKDD (2) 2019: 299-314 - [i7]Paul Prasse, René Knaebel, Lukás Machlica, Tomás Pevný, Tobias Scheffer:
Joint Detection of Malicious Domains and Infected Clients. CoRR abs/1906.09084 (2019) - [i6]Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer:
Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye. CoRR abs/1906.11889 (2019) - 2018
- [c72]Hanna Drimalla, Niels Landwehr, Irina Baskow, Behnoush Behnia, Stefan Roepke, Isabel Dziobek, Tobias Scheffer:
Detecting Autism by Analyzing a Simulated Social Interaction. ECML/PKDD (1) 2018: 193-208 - [c71]Silvia Makowski, Lena A. Jäger, Ahmed AbdelWahab, Niels Landwehr, Tobias Scheffer:
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements. ECML/PKDD (1) 2018: 209-225 - [i5]Silvia Makowski, Lena A. Jäger, Ahmed AbdelWahab, Niels Landwehr, Tobias Scheffer:
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements. CoRR abs/1809.08031 (2018) - 2017
- [j19]Matthias Bussas, Christoph Sawade, Nicolas Kühn, Tobias Scheffer, Niels Landwehr:
Varying-coefficient models for geospatial transfer learning. Mach. Learn. 106(9-10): 1419-1440 (2017) - [c70]Paul Prasse, Lukás Machlica, Tomás Pevný, Jirí Havelka, Tobias Scheffer:
Malware Detection by Analysing Encrypted Network Traffic with Neural Networks. ECML/PKDD (2) 2017: 73-88 - [c69]Paul Prasse, Lukás Machlica, Tomás Pevný, Jirí Havelka, Tobias Scheffer:
Malware Detection by Analysing Network Traffic with Neural Networks. IEEE Symposium on Security and Privacy Workshops 2017: 205-210 - 2016
- [j18]Uwe Dick, Tobias Scheffer:
Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers. Mach. Learn. 104(2-3): 385-410 (2016) - [c68]Oleksandr Zadorozhnyi, Gunthard Benecke, Stephan Mandt, Tobias Scheffer, Marius Kloft:
Huber-Norm Regularization for Linear Prediction Models. ECML/PKDD (1) 2016: 714-730 - 2015
- [j17]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to identify concise regular expressions that describe email campaigns. J. Mach. Learn. Res. 16: 3687-3720 (2015) - [c67]Michael Großhans, Tobias Scheffer:
Solving Prediction Games with Parallel Batch Gradient Descent. ECML/PKDD (1) 2015: 152-167 - [i4]Matthias Bussas, Christoph Sawade, Tobias Scheffer, Niels Landwehr:
Varying-coefficient models with isotropic Gaussian process priors. CoRR abs/1508.07192 (2015) - 2014
- [c66]Niels Landwehr, Sebastian Arzt, Tobias Scheffer, Reinhold Kliegl:
A Model of Individual Differences in Gaze Control During Reading. EMNLP 2014: 1810-1815 - [c65]Michael Großhans, Christoph Sawade, Tobias Scheffer, Niels Landwehr:
Joint Prediction of Topics in a URL Hierarchy. ECML/PKDD (1) 2014: 514-529 - 2013
- [j16]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr:
Active evaluation of ranking functions based on graded relevance. Mach. Learn. 92(1): 41-64 (2013) - [c64]Alexander Hewer, Joachim Weickert, Henning Seibert, Tobias Scheffer, Stefan Diebels:
Lagrangian Strain Tensor Computation with Higher Order Variational Models. BMVC 2013 - [c63]Michael Großhans, Christoph Sawade, Michael Brückner, Tobias Scheffer:
Bayesian Games for Adversarial Regression Problems. ICML (3) 2013: 55-63 - [c62]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr:
Active Evaluation of Ranking Functions Based on Graded Relevance (Extended Abstract). IJCAI 2013: 3072-3076 - 2012
- [j15]Michael Brückner, Christian Kanzow, Tobias Scheffer:
Static prediction games for adversarial learning problems. J. Mach. Learn. Res. 13: 2617-2654 (2012) - [c61]Peter Haider, Tobias Scheffer:
Finding Botnets Using Minimal Graph Clusterings. ICML 2012 - [c60]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to Identify Regular Expressions that Describe Email Campaigns. ICML 2012 - [c59]Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Active Comparison of Prediction Models. NIPS 2012: 1763-1771 - [c58]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr:
Active Evaluation of Ranking Functions Based on Graded Relevance. ECML/PKDD (2) 2012: 676-691 - [i3]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to Identify Regular Expressions that Describe Email Campaigns. CoRR abs/1206.4637 (2012) - [i2]Peter Haider, Tobias Scheffer:
Finding Botnets Using Minimal Graph Clusterings. CoRR abs/1206.4675 (2012) - 2011
- [c57]Michael Brückner, Tobias Scheffer:
Stackelberg games for adversarial prediction problems. KDD 2011: 547-555 - [e5]Lise Getoor, Tobias Scheffer:
Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. Omnipress 2011 [contents] - 2010
- [c56]Christoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer:
Active Risk Estimation. ICML 2010: 951-958 - [c55]Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer:
Throttling Poisson Processes. NIPS 2010: 505-513 - [c54]Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Active Estimation of F-Measures. NIPS 2010: 2083-2091
2000 – 2009
- 2009
- [j14]Szymon Jaroszewicz, Tobias Scheffer, Dan A. Simovici:
Scalable pattern mining with Bayesian networks as background knowledge. Data Min. Knowl. Discov. 18(1): 56-100 (2009) - [j13]Steffen Bickel, Michael Brückner, Tobias Scheffer:
Discriminative Learning Under Covariate Shift. J. Mach. Learn. Res. 10: 2137-2155 (2009) - [c53]Peter Haider, Tobias Scheffer:
Bayesian clustering for email campaign detection. ICML 2009: 385-392 - [c52]Michael Brückner, Tobias Scheffer:
Nash Equilibria of Static Prediction Games. NIPS 2009: 171-179 - [c51]Laura Dietz, Valentin Dallmeier, Andreas Zeller, Tobias Scheffer:
Localizing Bugs in Program Executions with Graphical Models. NIPS 2009: 468-476 - [r1]Tobias Scheffer:
Semi-Supervised Learning. Encyclopedia of Data Warehousing and Mining 2009: 1787-1793 - 2008
- [j12]Szymon Jaroszewicz, Lenka Ivantysynova, Tobias Scheffer:
Schema matching on streams with accuracy guarantees. Intell. Data Anal. 12(3): 253-270 (2008) - [c50]Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, Tobias Scheffer:
Multi-task learning for HIV therapy screening. ICML 2008: 56-63 - [c49]Uwe Dick, Peter Haider, Tobias Scheffer:
Learning from incomplete data with infinite imputations. ICML 2008: 232-239 - [c48]Steffen Bickel, Christoph Sawade, Tobias Scheffer:
Transfer Learning by Distribution Matching for Targeted Advertising. NIPS 2008: 145-152 - [c47]Thoralf Klein, Ulf Brefeld, Tobias Scheffer:
Exact and Approximate Inference for Annotating Graphs with Structural SVMs. ECML/PKDD (1) 2008: 611-623 - 2007
- [c46]Steffen Bickel, Michael Brückner, Tobias Scheffer:
Discriminative learning for differing training and test distributions. ICML 2007: 81-88 - [c45]Laura Dietz, Steffen Bickel, Tobias Scheffer:
Unsupervised prediction of citation influences. ICML 2007: 233-240 - [c44]Peter Haider, Ulf Brefeld, Tobias Scheffer:
Supervised clustering of streaming data for email batch detection. ICML 2007: 345-352 - [c43]Alexander Zien, Ulf Brefeld, Tobias Scheffer:
Transductive support vector machines for structured variables. ICML 2007: 1183-1190 - [c42]David S. Vogel, Ognian Asparouhov, Tobias Scheffer:
Scalable look-ahead linear regression trees. KDD 2007: 757-764 - [c41]Ulf Brefeld, Thoralf Klein, Tobias Scheffer:
Support Vector Machines for Collective Inference. MLG 2007 - 2006
- [c40]Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel:
Efficient co-regularised least squares regression. ICML 2006: 137-144 - [c39]Ulf Brefeld, Tobias Scheffer:
Semi-supervised learning for structured output variables. ICML 2006: 145-152 - [c38]Steffen Bickel, Tobias Scheffer:
Dirichlet-Enhanced Spam Filtering based on Biased Samples. NIPS 2006: 161-168 - [c37]Michael Brückner, Peter Haider, Tobias Scheffer:
Highly Scalable Discriminative Spam Filtering. TREC 2006 - [e4]Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou:
Machine Learning: ECML 2006, 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4212, Springer 2006, ISBN 3-540-45375-X [contents] - [e3]Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou:
Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4213, Springer 2006, ISBN 3-540-45374-1 [contents] - 2005
- [j11]Jörg Hakenberg, Steffen Bickel, Conrad Plake, Ulf Brefeld, Hagen Zahn, Lukas Faulstich, Ulf Leser, Tobias Scheffer:
Systematic feature evaluation for gene name recognition. BMC Bioinform. 6(S-1) (2005) - [j10]Tobias Scheffer:
Finding association rules that trade support optimally against confidence. Intell. Data Anal. 9(4): 381-395 (2005) - [j9]David S. Vogel, Steffen Bickel, Peter Haider, Rolf Schimpfky, Peter Siemen, Steve Bridges, Tobias Scheffer:
Classifying search engine queries using the web as background knowledge. SIGKDD Explor. 7(2): 117-122 (2005) - [c36]Steffen Bickel, Tobias Scheffer:
Estimation of Mixture Models Using Co-EM. ECML 2005: 35-46 - [c35]Ulf Brefeld, Christoph Büscher, Tobias Scheffer:
Multi-view Discriminative Sequential Learning. ECML 2005: 60-71 - [c34]Isabel Drost, Tobias Scheffer:
Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam. ECML 2005: 96-107 - [c33]Steffen Bickel, Peter Haider, Tobias Scheffer:
Learning to Complete Sentences. ECML 2005: 497-504 - [c32]Isabel Drost, Steffen Bickel, Tobias Scheffer:
Discovering Communities in Linked Data by Multi-view Clustering. GfKl 2005: 342-349 - [c31]Szymon Jaroszewicz, Tobias Scheffer:
Fast discovery of unexpected patterns in data, relative to a Bayesian network. KDD 2005: 118-127 - [c30]Ulf Brefeld, Christoph Büscher, Tobias Scheffer:
Multi-View Hidden Markov Perceptrons. LWA 2005: 134-138 - [c29]Steffen Bickel, Peter Haider, Tobias Scheffer:
Predicting Sentences using N-Gram Language Models. HLT/EMNLP 2005: 193-200 - [e2]Achim G. Hoffmann, Hiroshi Motoda, Tobias Scheffer:
Discovery Science, 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings. Lecture Notes in Computer Science 3735, Springer 2005, ISBN 3-540-29230-6 [contents] - [i1]Tobias Scheffer:
Multi-View Learning and Link Farm Discovery. Probabilistic, Logical and Relational Learning 2005 - 2004
- [j8]Tobias Scheffer:
Email answering assistance by semi-supervised text classification. Intell. Data Anal. 8(5): 481-493 (2004) - [j7]Mark-A. Krogel, Tobias Scheffer:
Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics. Mach. Learn. 57(1-2): 61-81 (2004) - [c28]Steffen Bickel, Tobias Scheffer:
Learning from Message Pairs for Automatic Email Answering. ECML 2004: 87-98 - [c27]Steffen Bickel, Tobias Scheffer:
Multi-View Clustering. ICDM 2004: 19-26 - [c26]Ulf Brefeld, Tobias Scheffer:
Co-EM support vector learning. ICML 2004 - [c25]Tobias Scheffer:
Workshop der GI-Fachgruppe "Maschinelles Lernen" (FGML). LWA 2004: 110 - [c24]Ulf Brefeld, Steffen Bickel, Tobias Scheffer:
Multi-View Lernen. LWA 2004: 131 - [c23]Isabel Drost, Tobias Scheffer:
Efficiency and Stability of Clustering Algorithms for Linked Data. LWA 2004: 146 - [c22]Korinna Grabski, Tobias Scheffer:
Sentence completion. SIGIR 2004: 433-439 - [e1]Andreas Abecker, Steffen Bickel, Ulf Brefeld, Isabel Drost, Nicola Henze, Olaf Herden, Mirjam Minor, Tobias Scheffer, Ljiljana Stojanovic, Stephan Weibelzahl:
LWA 2004: Lernen - Wissensentdeckung - Adaptivität, Berlin, 4. - 6. Oktober 2004, Workshopwoche der GI-Fachgruppen/Arbeitskreise (1) Fachgruppe Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen (ABIS 2004), (2) Arbeitskreis Knowledge Discovery (AKKD 2004), (3) Fachgruppe Maschinelles Lernen (FGML 2004), (4) Fachgruppe Wissens- und Erfahrungsmanagement (FGWM 2004). Humbold-Universität Berlin 2004 [contents] - 2003
- [c21]Mark-A. Krogel, Tobias Scheffer:
Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes. ICDM 2003: 569-572 - [c20]Michael Kockelkorn, Andreas Lüneburg, Tobias Scheffer:
Learning to Answer Emails. IDA 2003: 25-35 - [c19]Mark-A. Krogel, Tobias Scheffer:
Effectiveness of information extraction, multi-relational, and multi-view learning for prediction gene deletion experiments. BIOKDD 2003: 10-16 - [c18]Michael Kockelkorn, Andreas Lüneburg, Tobias Scheffer:
Using Transduction and Multi-view Learning to Answer Emails. PKDD 2003: 266-277 - 2002
- [j6]Tobias Scheffer, Stefan Wrobel:
Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. J. Mach. Learn. Res. 3: 833-862 (2002) - [j5]Tobias Scheffer, Stefan Wrobel, Borislav Popov, Damyan Ognianov, Christian Decomain, Susanne Hoche:
Lerning Hidden Markov Models for Information Extraction Actively from Partially Labeled Text. Künstliche Intell. 16(2): 17-22 (2002) - [j4]Mark-A. Krogel, Marcus Denecke, Marco Landwehr, Tobias Scheffer:
Combining Data and Text Mining Techniques for Yeast Gene Regulation Prediction: A Case Study. SIGKDD Explor. 4(2): 104-105 (2002) - [c17]Tobias Scheffer, Stefan Wrobel:
A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases. PKDD 2002: 397-409 - 2001
- [c16]Hans Gründel, Tino Naphtali, Christian Wiech, Jan-Marian Gluba, Maiken Rohdenburg, Tobias Scheffer:
Clipping and Analyzing News Using Machine Learning Techniques. Discovery Science 2001: 87-99 - [c15]Tobias Scheffer, Christian Decomain, Stefan Wrobel:
Mining the Web with Active Hidden Markov Models. ICDM 2001: 645-646 - [c14]Tobias Scheffer, Stefan Wrobel:
Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems. ICML 2001: 481-488 - [c13]Tobias Scheffer, Christian Decomain, Stefan Wrobel:
Active Hidden Markov Models for Information Extraction. IDA 2001: 309-318 - [c12]Tobias Scheffer:
Finding Association Rules That Trade Support Optimally against Confidence. PKDD 2001: 424-435 - 2000
- [c11]Tobias Scheffer:
Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees. ALT 2000: 194-208 - [c10]Tobias Scheffer:
Nonparametric Regularization of Decision Trees. ECML 2000: 344-356 - [c9]Tobias Scheffer:
Predicting the Generalization Performance of Cross Validatory Model Selection Criteria. ICML 2000: 831-838 - [c8]Tobias Scheffer, Stefan Wrobel:
A sequential sampling algorithm for a general class of utility criteria. KDD 2000: 330-334
1990 – 1999
- 1999
- [b1]Tobias Scheffer:
Error estimation and model selection. Technical University of Berlin, Germany, DISKI 225, Infix 1999, ISBN 978-3-89601-225-8, pp. I-XI, 1-126 - [j3]Tobias Scheffer:
Error Estimation and Model Selection. Künstliche Intell. 13(3): 46-48 (1999) - [j2]Tobias Scheffer:
International Conference on Machine Learning (ICML-99). Künstliche Intell. 13(4): 68 (1999) - [c7]Andrew R. Mitchell, Tobias Scheffer, Arun Sharma, Frank Stephan:
The VC-Dimension of Subclasses of Pattern. ALT 1999: 93-105 - [c6]Tobias Scheffer, Thorsten Joachims:
Expected Error Analysis for Model Selection. ICML 1999: 361-370 - 1998
- [c5]Tobias Scheffer, Thorsten Joachims:
Estimating the Expected Error of Empirical Minimizers for Model Selection. AAAI/IAAI 1998: 1200 - 1997
- [c4]Tobias Scheffer, Russell Greiner, Christian Darken:
Why Experimentation can be better than "Perfect Guidance". ICML 1997: 331-339 - [c3]Tobias Scheffer, Ralf Herbrich:
Unbiased Assesment of Learning Algorithms. IJCAI (2) 1997: 798-803 - 1996
- [j1]Marion Finke, Günter Hommel, Tobias Scheffer, Fritz Wysotzki:
Aerial Robotics in Computer Science Education. Comput. Sci. Educ. 7(2): 239-246 (1996) - [c2]Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki:
Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996: 212-228 - 1995
- [c1]Tobias Scheffer:
A Generic Algorithm for Learning Rules with Hierarchical Exceptions. SBIA 1995: 181-190
Coauthor Index
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