Ute Schmid
Ute Schmid
Professor for Applied Computer Science/Cognitive Systems, Universitaet Bamberg
Bestätigte E-Mail-Adresse bei uni-bamberg.de - Startseite
Zitiert von
Zitiert von
Handbuch der Künstlichen Intelligenz
G Görz, U Schmid, T Braun
Handbuch der Künstlichen Intelligenz
G Görz, J Schneeberger, U Schmid
Inductive programming meets the real world
S Gulwani, J Hernández-Orallo, E Kitzelmann, SH Muggleton, U Schmid, ...
Communications of the ACM 58 (11), 90-99, 2015
Inductive synthesis of functional programs: An explanation based generalization approach.
E Kitzelmann, U Schmid, R Olsson, LP Kaelbling
Journal of Machine Learning Research 7 (2), 2006
Metaphors and heuristic-driven theory projection (HDTP)
H Gust, KU Kühnberger, U Schmid
Theoretical Computer Science 354 (1), 98-117, 2006
Who should test whom?
S McDonald, HM Edwards
Communications of the ACM 50 (1), 66-71, 2007
Ultra-strong machine learning: comprehensibility of programs learned with ILP
SH Muggleton, U Schmid, C Zeller, A Tamaddoni-Nezhad, T Besold
Machine Learning 107 (7), 1119-1140, 2018
Learning recursive control programs from problem solving.
P Langley, D Choi, R Olsson, U Schmid
Journal of Machine Learning Research 7 (3), 2006
Inductive rule learning on the knowledge level
U Schmid, E Kitzelmann
Cognitive Systems Research 12 (3-4), 237-248, 2011
Computer models solving intelligence test problems: Progress and implications
J Hernández-Orallo, F Martínez-Plumed, U Schmid, M Siebers, DL Dowe
Artificial Intelligence 230, 74-107, 2016
Inductive synthesis of functional programs: universal planning, folding of finite programs, and schema abstraction by analogical reasoning
U Schmid
Springer Science & Business Media, 2003
The challenge of complexity for cognitive systems
U Schmid, M Ragni, C Gonzalez, J Funke
Cognitive Systems Research 12 (3-4), 211-218, 2011
Induction of recursive program schemes
U Schmid, F Wysotzki
European Conference on Machine Learning, 214-225, 1998
Particle swarm optimization
T Zeugmann, P Poupart, J Kennedy, X Jin, J Han, L Saitta, M Sebag, ...
Encyclopedia of machine learning 1 (1), 760-766, 2011
An algebraic framework for solving proportional and predictive analogies
U Schmid, H Gust, KU Kühnberger, J Burghardt
Proceedings of EuroCogSci 03, 295-300, 2019
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting.
A Passerini, P Frasconi, L De Raedt, R Olsson, U Schmid
Journal of Machine Learning Research 7 (2), 2006
How does predicate invention affect human comprehensibility?
U Schmid, C Zeller, T Besold, A Tamaddoni-Nezhad, S Muggleton
International Conference on Inductive Logic Programming, 52-67, 2016
An introduction to inductive programming
P Flener, U Schmid
Artificial Intelligence Review 29 (1), 45-62, 2008
A unifying framework for analysis and evaluation of inductive programming systems
M Hofmann, E Kitzelmann, U Schmid
Proceedings of the Second Conference on Artificial General Intelligence, 55-60, 2009
Applying Inductive Program Synthesis to Macro Learning.
U Schmid, F Wysotzki
AIPS, 371-378, 2000
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