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ORCIDUte Schmid
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- affiliation: University of Bamberg, WIAI, Germany
- affiliation: University of Osnabrück, Institute of Computer Science, Germany
- affiliation: TU Berlin, Department of Computer Science, Germany
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2020 – today
- 2022
[j39]Johannes Rabold
, Michael Siebers, Ute Schmid:
Generating contrastive explanations for inductive logic programming based on a near miss approach. Mach. Learn. 111(5): 1799-1820 (2022)- [j38]Joscha Eirich, Jakob Bonart, Dominik Jäckle, Michael Sedlmair, Ute Schmid, Kai Fischbach, Tobias Schreck, Jürgen Bernard:
IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines. IEEE Trans. Vis. Comput. Graph. 28(1): 11-21 (2022) - [c63]Dennis Müller, Michael März, Stephan Scheele, Ute Schmid:
An Interactive Explanatory AI System for Industrial Quality Control. AAAI 2022: 12580-12586 - [c62]Kolja Kühnlenz, Ute Schmid, Barbara Kühnlenz:
A Video-based Study on Perceived Intelligence, Subjective Performance and Trust under Variation of Prior Information given to Users in Autonomous Driving. ARSO 2022: 1-4 - [c61]Jaspar Pahl, Ines Rieger, Anna Möller, Thomas Wittenberg, Ute Schmid:
Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces. FAccT 2022: 973-987 - [c60]Marvin Herchenbach, Dennis Müller, Stephan Scheele, Ute Schmid:
Explaining Image Classifications with Near Misses, Near Hits and Prototypes - Supporting Domain Experts in Understanding Decision Boundaries. ICPRAI (2) 2022: 419-430 - [c59]Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid:
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. AIAI Workshops 2022: 389-400 - [i19]Gesina Schwalbe, Christian Wirth, Ute Schmid:
Concept Embeddings for Fuzzy Logic Verification of Deep Neural Networks in Perception Tasks. CoRR abs/2201.00572 (2022) - [i18]Dennis Müller, Michael März, Stephan Scheele, Ute Schmid:
An Interactive Explanatory AI System for Industrial Quality Control. CoRR abs/2203.09181 (2022) - [i17]Christoph Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid:
Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation. CoRR abs/2204.01292 (2022) - [i16]Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid:
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. CoRR abs/2204.02661 (2022) - [i15]Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid:
Explanatory machine learning for sequential human teaching. CoRR abs/2205.10250 (2022) - 2021
- [j37]Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and harmful explanatory machine learning. Mach. Learn. 110(4): 695-721 (2021) - [j36]Teena Hassan, Dominik Seuß, Johannes Wollenberg, Katharina Weitz, Miriam Kunz, Stefan Lautenbacher, Jens-Uwe Garbas, Ute Schmid:
Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(6): 1815-1831 (2021) - [c58]Bettina Finzel, David Elias Tafler, Anna Magdalena Thaler, Ute Schmid:
Multimodal Explanations for User-centric Medical Decision Support Systems. HUMAN@AAAI Fall Symposium 2021 - [c57]Deniz Neufeld, Ute Schmid:
Anomaly Detection for Hydraulic Systems under Test. ETFA 2021: 1-8 - [c56]Ute Schmid, Anja Gärtig-Daugs, Linda Müller, Alexander Werner:
Grundkonzepte des Maschinellen Lernens für die Grundschule - Algorithmen, Biases, Generalisierungsfehler. GI-Jahrestagung 2021: 1611-1623 - [c55]Sascha Lang, Valentin Plenk, Ute Schmid:
A Case-Based Reasoning Approach for a Decision Support System in Manufacturing. IEA/AIE (2) 2021: 265-271 - [c54]Bettina Finzel, David E. Tafler, Stephan Scheele, Ute Schmid:
Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations. KI 2021: 80-94 - [c53]Bettina Finzel, René Kollmann, Ines Rieger, Jaspar Pahl, Ute Schmid:
Deriving Temporal Prototypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification. LWDA 2021: 86-97 - [c52]Jonas-Dario Troles, Ute Schmid:
Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives. WMT@EMNLP 2021: 531-541 - [e5]Martin Atzmüller, Tomás Kliegr, Ute Schmid:
Proceedings of the First International Workshop on Explainable and Interpretable Machine Learning (XI-ML 2020) co-located with the 43rd German Conference on Artificial Intelligence (KI 2020), Bamberg, Germany, September 21, 2020 (Virtual Workshop). CEUR Workshop Proceedings 2796, CEUR-WS.org 2021 [contents] - [i14]Johannes Rabold, Gesina Schwalbe, Ute Schmid:
Expressive Explanations of DNNs by Combining Concept Analysis with ILP. CoRR abs/2105.07371 (2021) - [i13]Johannes Rabold, Michael Siebers, Ute Schmid:
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach. CoRR abs/2106.08064 (2021) - [i12]Jonas-Dario Troles, Ute Schmid:
Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives. CoRR abs/2107.11584 (2021) - [i11]Bettina Finzel, David E. Tafler, Stephan Scheele, Ute Schmid:
Explanation as a process: user-centric construction of multi-level and multi-modal explanations. CoRR abs/2110.03759 (2021) - [i10]Andrew Cropper, Luc De Raedt, Richard Evans, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192). Dagstuhl Reports 11(4): 20-33 (2021) - 2020
- [j35]Mark Gromowski, Michael Siebers, Ute Schmid:
A process framework for inducing and explaining Datalog theories. Adv. Data Anal. Classif. 14(4): 821-835 (2020) - [j34]Sebastian Bruckert, Bettina Finzel, Ute Schmid:
The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions. Frontiers Artif. Intell. 3: 507973 (2020) - [j33]Ute Schmid, Bettina Finzel:
Mutual Explanations for Cooperative Decision Making in Medicine. Künstliche Intell. 34(2): 227-233 (2020) - [c51]Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid:
Verifying Deep Learning-based Decisions for Facial Expression Recognition. ESANN 2020: 139-144 - [c50]Ute Schmid, Volker Tresp, Matthias Bethge, Kristian Kersting, Rainer Stiefelhagen:
Künstliche Intelligenz - Die dritte Welle. GI-Jahrestagung 2020: 91-95 - [c49]Johannes Rabold, Gesina Schwalbe, Ute Schmid:
Expressive Explanations of DNNs by Combining Concept Analysis with ILP. KI 2020: 148-162 - [c48]Ute Schmid:
AI goes to school: learning about and learning with artificial intelligence. WiPSCE 2020: 2:1 - [p4]Günther Görz, Tanya Braun, Ute Schmid:
Einleitung. Handbuch der Künstlichen Intelligenz 2020: 1-26 - [e4]Günther Görz, Ute Schmid, Tanya Braun:
Handbuch der Künstlichen Intelligenz, 6. Auflage. De Gruyter 2020, ISBN 9783110659948 [contents] - [e3]Ute Schmid, Franziska Klügl, Diedrich Wolter:
KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Bamberg, Germany, September 21-25, 2020, Proceedings. Lecture Notes in Computer Science 12325, Springer 2020, ISBN 978-3-030-58284-5 [contents] - [i9]Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid:
Verifying Deep Learning-based Decisions for Facial Expression Recognition. CoRR abs/2003.00828 (2020) - [i8]Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and Harmful Explanatory Machine Learning. CoRR abs/2009.06410 (2020)
2010 – 2019
- 2019
- [j32]Michael Siebers, Ute Schmid:
Please delete that! Why should I? - Explaining learned irrelevance classifications of digital objects. Künstliche Intell. 33(1): 35-44 (2019) - [c47]Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid:
Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data. PKDD/ECML Workshops (1) 2019: 147-158 - [c46]Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid:
Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. PKDD/ECML Workshops (1) 2019: 180-192 - [c45]Anja Gärtig-Daugs, Alexander Werner, Ute Schmid:
"Wie funktioniert das?" - Informatische Konzepte in der Vor- und Grundschule spielerisch begreifen und anwenden. INFOS 2019: 377 - [i7]Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid:
Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. CoRR abs/1910.01837 (2019) - [i6]Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid:
Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data. CoRR abs/1910.07856 (2019) - [i5]Luc De Raedt, Richard Evans, Stephen H. Muggleton, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). Dagstuhl Reports 9(5): 58-88 (2019) - 2018
- [j31]Ute Schmid, Katharina Weitz, Anja Gärtig-Daugs:
Informatik in der Grundschule - Eine informatisch-pädagogische Perspektive auf informatikdidaktische Konzepte. Inform. Spektrum 41(3): 200-207 (2018) - [j30]Stephen H. Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, Tarek R. Besold:
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Mach. Learn. 107(7): 1119-1140 (2018) - [c44]Bettina Finzel, Hannah Deininger, Ute Schmid:
From beliefs to intention: mentoring as an approach to motivate female high school students to enrol in computer science studies. GenderIT 2018: 251-260 - [c43]Johannes Rabold, Michael Siebers, Ute Schmid:
Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules. ILP 2018: 105-117 - [c42]Michael Siebers, Ute Schmid:
Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. ILP 2018: 141-156 - [c41]Ute Schmid:
Inductive Programming as Approach to Comprehensible Machine Learning. DKB/KIK@KI 2018: 4-12 - [c40]Ingo J. Timm, Steffen Staab, Michael Siebers, Claudia Schon, Ute Schmid, Kai Sauerwald, Lukas Reuter, Marco Ragni, Claudia Niederée, Heiko Maus, Gabriele Kern-Isberner, Christian Jilek, Paulina Friemann, Thomas Eiter, Andreas Dengel, Hannah Dames, Tanja Bock, Jan Ole Berndt, Christoph Beierle:
Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges. KI 2018: 357-365 - 2017
- [j29]Aboubakr Benabbas, Golnaz Elmamooz, Brent Lagesse, Daniela Nicklas, Ute Schmid:
Living Lab Bamberg: an infrastructure to explore smart city research challenges in the wild. Künstliche Intell. 31(3): 265-271 (2017) - [c39]Frederick Birnbaum, Christian Moewes, Daniela Nicklas, Ute Schmid:
Data Mining von multidimensionalen Qualitätsdaten aus einer computerintegrierten industriellen Fertigung zur visuellen Analyse von komplexen Wirkzusammenhängen. BTW (Workshops) 2017: 139-142 - [c38]Christina Zeller, Ute Schmid:
The Impact of Presentation Order on Category Learning Strategies: Behavioral Data and Self-Reports. CogSci 2017 - [c37]Michael Siebers, Kyra Gobel, Cornelia Niessen, Ute Schmid:
Requirements for a companion system to support identifying irrelevancy. ICCT 2017: 1-2 - [c36]José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe:
Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract). IJCAI 2017: 5005-5009 - [c35]Christina Zeller, Ute Schmid:
A Human Like Incremental Decision Tree Algorithm: Combining Rule Learning, Pattern Induction, and Storing Examples. LWDA 2017: 64 - [c34]Anja Gärtig-Daugs, Katharina Weitz, Ute Schmid:
Kindliche Modelle der digitalen Welt. INFOS 2017: 419-420 - [c33]Katharina Weitz, Anja Gärtig-Daugs, Daniel Knauf, Ute Schmid:
Computer Science in Early Childhood Education: Pedagogical Beliefs and Perceived Self-Confidence in Preschool Teachers. WiPSCE 2017: 117-118 - [c32]Maike Wolking, Ute Schmid:
Mental Models, Career Aspirations, and the Acquirement of Basic Concepts of Computer Science in Elementary Education: Empirical Evaluation of the Computer Science Experimenter's Kit. WiPSCE 2017: 119-120 - [r6]Pierre Flener, Ute Schmid:
Inductive Programming. Encyclopedia of Machine Learning and Data Mining 2017: 658-666 - [r5]Pierre Flener, Ute Schmid:
Programming by Demonstration. Encyclopedia of Machine Learning and Data Mining 2017: 1017-1018 - [r4]Pierre Flener, Ute Schmid:
Trace-Based Programming. Encyclopedia of Machine Learning and Data Mining 2017: 1281-1282 - [i4]Ute Schmid, Stephen H. Muggleton, Rishabh Singh:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). Dagstuhl Reports 7(9): 86-108 (2017) - 2016
- [j28]José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe:
Computer models solving intelligence test problems: Progress and implications. Artif. Intell. 230: 74-107 (2016) - [j27]Michael Siebers, Ute Schmid, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher:
Characterizing facial expressions by grammars of action unit sequences - A first investigation using ABL. Inf. Sci. 329: 866-875 (2016) - [c31]Teena Hassan, Dominik Seuss, Johannes Wollenberg, Jens-Uwe Garbas, Ute Schmid:
A Practical Approach to Fuse Shape and Appearance Information in a Gaussian Facial Action Estimation Framework. ECAI 2016: 1812-1817 - [c30]Daniel Hallmann, Ute Schmid, Rüdiger von der Weth:
Gemeinsame mentale Modelle in der agilen Softwareentwicklung: Ein Ansatz zur Erstellung von Gestaltungsempfehlungen für "gute" erfahrungsspezifische User Stories. GI-Jahrestagung 2016: 1969-1974 - [c29]Michael Siebers, Franz Uhrmann, Oliver Scholz, Christoph Stocker, Ute Schmid:
Automatische Detektion von Trockenstress bei Tabakpflanzen mittels Machine-Learning-Verfahren. GIL Jahrestagung 2016: 197-200 - [c28]Christina Zeller, Ute Schmid:
Automatic Generation of Analogous Problems to Help Resolving Misconceptions in an Intelligent Tutor System for Written Subtraction. ICCBR Workshops 2016: 108-117 - [c27]Ute Schmid, Christina Zeller, Tarek R. Besold, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
How Does Predicate Invention Affect Human Comprehensibility? ILP 2016: 52-67 - [c26]Anja Gärtig-Daugs, Katharina Weitz, Maike Wolking, Ute Schmid:
Computer science experimenter's kit for use in preschool and primary school. WiPSCE 2016: 66-71 - 2015
- [j26]Sumit Gulwani, José Hernández-Orallo, Emanuel Kitzelmann, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn:
Inductive programming meets the real world. Commun. ACM 58(11): 90-99 (2015) - [j25]Ute Schmid, Anja Gärtig-Daugs, Silvia Förtsch:
Introvertierte Studenten, fleißige Studentinnen? - Geschlechtsspezifische Unterschiede in Motivation, Zufriedenheit und Wahrnehmungsmustern bei Informatikstudierenden - Ergebnisse aus Erstsemesterbefragungen an der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg differenziert nach Geschlecht. Inform. Spektrum 38(5): 379-395 (2015) - [j24]Ute Schmid:
You Need the AI Community - and the AI Community Needs You! Künstliche Intell. 29(3): 239-241 (2015) - [j23]Tarek R. Besold, José Hernández-Orallo, Ute Schmid:
Can Machine Intelligence be Measured in the Same Way as Human intelligence? Künstliche Intell. 29(3): 291-297 (2015) - [c25]Ute Schmid, Marco Ragni:
Comparing Computer Models Solving Number Series Problems. AGI 2015: 352-361 - [c24]Ute Schmid:
Cognitive Systems: Goals, Approaches, Applications. GI-Jahrestagung 2015: 1251-1252 - [i3]José Hernández-Orallo, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 15442). Dagstuhl Reports 5(10): 89-111 (2015) - 2014
- [j22]Ute Schmid:
Does AI Need a New Debate on Ethics? Künstliche Intell. 28(1): 1-3 (2014) - [c23]Jacqueline Hofmann, Emanuel Kitzelmann, Ute Schmid:
Applying Inductive Program Synthesis to Induction of Number Series A Case Study with IGOR2. KI 2014: 25-36 - [p3]Ute Schmid, Lukas Berle, Michael Munz, Klaus Stein, Martin Sticht:
How Similar is What I Get to What I Want: Matchmaking for Mobility Support. Computational Approaches to Analogical Reasoning 2014: 263-287 - [i2]Mark Wernsdorfer, Ute Schmid:
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer. CoRR abs/1412.6451 (2014) - 2013
- [j21]Ute Schmid, Michael Siebers, Johannes Folger, Simone Schineller, Dominik Seuß, Marius Raab, Claus-Christian Carbon, Stella J. Faerber:
A cognitive model for predicting esthetical judgements as similarity to dynamic prototypes. Cogn. Syst. Res. 24: 72-79 (2013) - [j20]Christoph Schlieder, Ute Schmid, Michael Munz, Klaus Stein:
Assistive Technology to Support the Mobility of Senior Citizens - Overcoming Mobility Barriers and Establishing Mobility Chains by Social Collaboration. Künstliche Intell. 27(3): 247-253 (2013) - [c22]Christoph Stocker, Michael Siebers, Ute Schmid:
Erkennung von Sequenzen mimischer Schmerzausdrücke durch genetische Programmierung. LWA 2013: 117-120 - [c21]Christoph Stocker, Franz Uhrmann, Oliver Scholz, Michael Siebers, Ute Schmid:
A machine learning approach to drought stress level classification of tobacco plants. LWA 2013: 163-167 - [p2]Mark Wernsdorfer, Ute Schmid:
From Streams of Observations to Knowledge-Level Productive Predictions. Human Behavior Recognition Technologies 2013: 268-281 - [p1]Günther Görz, Ute Schmid, Ipke Wachsmuth:
Einleitung. Handbuch der Künstlichen Intelligenz 2013: 1-18 - [e2]Günther Görz, Josef Schneeberger, Ute Schmid:
Handbuch der Künstlichen Intelligenz, 5. Auflage. Oldenbourg Wissenschaftsverlag 2013, ISBN 9783486719796 [contents] - [i1]Sumit Gulwani, Emanuel Kitzelmann, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 13502). Dagstuhl Reports 3(12): 43-66 (2013) - 2012
- [j19]Ute Schmid:
KI und Informatik. Künstliche Intell. 26(1): 1-4 (2012) - [c20]Linn Gralla, Thora Tenbrink, Michael Siebers, Ute Schmid:
Analogical Problem Solving: Insights from Verbal Reports. CogSci 2012 - [c19]Michael Siebers, Ute Schmid:
Semi-analytic Natural Number Series Induction. KI 2012: 249-252 - [c18]Ute Schmid, Michael Siebers, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher:
Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain. ICGI 2012: 183-188 - 2011
- [j18]Ute Schmid, Marco Ragni, Cleotilde Gonzalez, Joachim Funke:
The challenge of complexity for cognitive systems. Cogn. Syst. Res. 12(3-4): 211-218 (2011) - [j17]Ute Schmid, Emanuel Kitzelmann:
Inductive rule learning on the knowledge level. Cogn. Syst. Res. 12(3-4): 237-248 (2011) - [c17]Marius Raab, Mark Wernsdorfer, Emanuel Kitzelmann, Ute Schmid:
From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience. AGI 2011: 333-339 - 2010
- [c16]Christine Barthold, Anton Papst, Thomas Wittenberg, Christian Küblbeck, Stefan Lautenbacher, Ute Schmid, Sven Friedl:
Tracking von Gesichtsmimik mit Hilfe von Gitterstrukturen zur Klassifikation von schmerzrelevanten Action Units. Bildverarbeitung für die Medizin 2010: 455-459 - [c15]Martin Hofmann, Ute Schmid:
Data-Driven Detection of Recursive Program Schemes. ECAI 2010: 1063-1064 - [c14]Michael Siebers, Ute Schmid:
Interleaving Forward Backward Feature Selection. KDIR 2010: 454-457 - [c13]Ute Schmid, Martin Hofmann, Florian Bader, Tilmann Häberle, Thomas Schneider:
Incident Mining Using Structural Prototypes. IEA/AIE (2) 2010: 327-336 - [e1]Ute Schmid, Emanuel Kitzelmann, Rinus Plasmeijer:
Approaches and Applications of Inductive Programming, Third International Workshop, AAIP 2009, Edinburgh, UK, September 4, 2009. Revised Papers. Lecture Notes in Computer Science 5812, Springer 2010, ISBN 978-3-642-11930-9 [contents] - [r3]Pierre Flener, Ute Schmid:
Inductive Programming. Encyclopedia of Machine Learning 2010: 537-544 - [r2]Pierre Flener, Ute Schmid:
Programming by Demonstration. Encyclopedia of Machine Learning 2010: 805 - [r1]Pierre Flener, Ute Schmid:
Trace-Based Programming. Encyclopedia of Machine Learning 2010: 989
2000 – 2009
- 2009
- [j16]Ute Schmid, Martin Hofmann, Emanuel Kitzelmann:
Inductive Programming. Künstliche Intell. 23(2): 38-41 (2009) - [j15]Ute Schmid:
Editorial 3-2009. Künstliche Intell. 23(3): 1 (2009) - [c12]Martin Hofmann, Emanuel Kitzelmann, Ute Schmid:
Porting IgorII from Maude to Haskell. AAIP 2009: 140-158 - [c11]Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, Ute Schmid:
Evolutionary Programming Guided by Analytically Generated Seeds. IJCCI 2009: 198-203 - 2008
- [b3]Jörg Mennicke, Christian Münzenmayer, Ute Schmid:
Classifier Learning for Imbalanced Data - a Comparison of kNN, SVM, and Decision Tree Learning. VDM 2008, ISBN 978-3-8364-9223-2, pp. I-IX, 1-164 - [j14]Pierre Flener, Ute Schmid:
An introduction to inductive programming. Artif. Intell. Rev. 29(1): 45-62 (2008) - [j13]Ute Schmid:
Gasteditorial KI und Kognition. Künstliche Intell. 22(1): 4 (2008) - [j12]Ute Schmid:
Cognition and AI. Künstliche Intell. 22(1): 5-7 (2008) - [j11]Ute Schmid:
Interview with Ken Forbus. Künstliche Intell. 22(1): 37-38 (2008) - [j10]Ute Schmid:
KI und Kognition - Service. Künstliche Intell. 22(1): 45 (2008) - [j9]Ute Schmid:
Editorial. Künstliche Intell. 22(2): 1 (2008) - [j8]Ute Schmid:
KogWis 2008. Künstliche Intell. 22(2): 78 (2008) - [c10]Martin Hofmann, Emanuel Kitzelmann, Ute Schmid:
Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework. KI 2008: 78-86 - 2007
- [j7]Sven-Eric Schelhorn, Jacqueline Griego, Ute Schmid:
Transformational and derivational strategies in analogical problem solving. Cogn. Process. 8(1): 45-55 (2007) - [j6]Emanuel Kitzelmann, Ute Schmid:
Inducing Constructor Systems from Example-Terms by Detecting Syntactical Regularities. Electron. Notes Theor. Comput. Sci. 174(1): 49-63 (2007) - [c9]Martin Hofmann, Andreas Hirschberger, Emanuel Kitzelmann, Ute Schmid:
Inductive Synthesis of Recursive Functional Programs. KI 2007: 468-472 - 2006
- [j5]