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Michael Siebers
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2020 – today
- 2022
- [j7]Johannes Rabold
, Michael Siebers
Generating contrastive explanations for inductive logic programming based on a near miss approach. Mach. Learn. 111(5): 1799-1820 (2022) - 2021
- [i2]Johannes Rabold, Michael Siebers, Ute Schmid:
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach. CoRR abs/2106.08064 (2021) - 2020
- [j6]Mark Gromowski
A process framework for inducing and explaining Datalog theories. Adv. Data Anal. Classif. 14(4): 821-835 (2020)
2010 – 2019
- 2019
- [j5]Michael Siebers
Please delete that! Why should I? - Explaining learned irrelevance classifications of digital objects. Künstliche Intell. 33(1): 35-44 (2019) - [c14]Johannes Rabold, Hannah Deininger
Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. PKDD/ECML Workshops (1) 2019: 180-192 - [i1]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) - 2018
- [c13]Johannes Rabold, Michael Siebers
Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules. ILP 2018: 105-117 - [c12]Michael Siebers
Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. ILP 2018: 141-156 - [c11]Ingo J. Timm, Steffen Staab, Michael Siebers
Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges. KI 2018: 357-365 - 2017
- [c10]Michael Siebers
Requirements for a companion system to support identifying irrelevancy. ICCT 2017: 1-2 - [c9]José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers
Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract). IJCAI 2017: 5005-5009 - 2016
- [j4]José Hernández-Orallo, Fernando Martínez-Plumed
Computer models solving intelligence test problems: Progress and implications. Artif. Intell. 230: 74-107 (2016) - [j3]Michael Siebers
Characterizing facial expressions by grammars of action unit sequences - A first investigation using ABL. Inf. Sci. 329: 866-875 (2016) - [c8]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 - 2014
- [j2]Michael Siebers
Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions. Künstliche Intell. 28(1): 35-38 (2014) - 2013
- [j1]Ute Schmid
A cognitive model for predicting esthetical judgements as similarity to dynamic prototypes. Cogn. Syst. Res. 24: 72-79 (2013) - [c7]Christoph Stocker, Michael Siebers, Ute Schmid:
Erkennung von Sequenzen mimischer Schmerzausdrücke durch genetische Programmierung. LWA 2013: 117-120 - [c6]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 - 2012
- [c5]Linn Gralla, Thora Tenbrink, Michael Siebers, Ute Schmid:
Analogical Problem Solving: Insights from Verbal Reports. CogSci 2012 - [c4]Michael Siebers
Semi-analytic Natural Number Series Induction. KI 2012: 249-252 - [c3]Ute Schmid, Michael Siebers
Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain. ICGI 2012: 183-188 - 2011
- [c2]Michael Siebers:
Combining Supervised and Unsupervised Learning - An Ensemble Learning Framework Proposal. LWA 2011: 32-34 - 2010
- [c1]Michael Siebers, Ute Schmid:
Interleaving Forward Backward Feature Selection. KDIR 2010: 454-457
Coauthor Index
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