| 2009 |
|---|
| 85 | EE | Frank J. Balbach,
Thomas Zeugmann:
Recent Developments in Algorithmic Teaching.
LATA 2009: 1-18 |
| 2008 |
|---|
| 84 | | Yoav Freund,
László Györfi,
György Turán,
Thomas Zeugmann:
Algorithmic Learning Theory, 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
Springer 2008 |
| 83 | EE | Skip Jordan,
Thomas Zeugmann:
Indistinguishability and First-Order Logic.
TAMC 2008: 94-104 |
| 82 | EE | Yohji Akama,
Thomas Zeugmann:
Consistent and coherent learning with delta-delay.
Inf. Comput. 206(11): 1362-1374 (2008) |
| 81 | EE | John Case,
Takeshi Shinohara,
Thomas Zeugmann,
Sandra Zilles:
Foreword.
Theor. Comput. Sci. 397(1-3): 1-3 (2008) |
| 80 | EE | Steffen Lange,
Thomas Zeugmann,
Sandra Zilles:
Learning indexed families of recursive languages from positive data: A survey.
Theor. Comput. Sci. 397(1-3): 194-232 (2008) |
| 79 | EE | Thomas Zeugmann,
Sandra Zilles:
Learning recursive functions: A survey.
Theor. Comput. Sci. 397(1-3): 4-56 (2008) |
| 2007 |
|---|
| 78 | EE | Shai Ben-David,
John Case,
Thomas Zeugmann:
Foreword.
Theor. Comput. Sci. 382(3): 167-169 (2007) |
| 2006 |
|---|
| 77 | EE | Frank J. Balbach,
Thomas Zeugmann:
Teaching Memoryless Randomized Learners Without Feedback.
ALT 2006: 93-108 |
| 76 | EE | Frank J. Balbach,
Thomas Zeugmann:
Teaching Randomized Learners.
COLT 2006: 229-243 |
| 75 | EE | Frank J. Balbach,
Thomas Zeugmann:
On the Teachability of Randomized Learners.
Complexity of Boolean Functions 2006 |
| 74 | EE | Jan Poland,
Thomas Zeugmann:
Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts.
Discovery Science 2006: 197-208 |
| 73 | EE | Yohji Akama,
Thomas Zeugmann:
Consistency Conditions for Inductive Inference of Recursive Functions.
JSAI 2006: 251-264 |
| 72 | EE | Ryutaro Kurai,
Shin-ichi Minato,
Thomas Zeugmann:
N-Gram Analysis Based on Zero-Suppressed BDDs.
JSAI 2006: 289-300 |
| 71 | EE | Thomas Zeugmann:
Inductive Inference and Language Learning.
TAMC 2006: 464-473 |
| 70 | EE | Nicolò Cesa-Bianchi,
Rüdiger Reischuk,
Thomas Zeugmann:
Foreword.
Theor. Comput. Sci. 350(1): 1-2 (2006) |
| 69 | EE | John Case,
Sanjay Jain,
Rüdiger Reischuk,
Frank Stephan,
Thomas Zeugmann:
Learning a subclass of regular patterns in polynomial time.
Theor. Comput. Sci. 364(1): 115-131 (2006) |
| 68 | EE | Thomas Zeugmann:
From learning in the limit to stochastic finite learning.
Theor. Comput. Sci. 364(1): 77-97 (2006) |
| 2005 |
|---|
| 67 | EE | Frank J. Balbach,
Thomas Zeugmann:
Teaching Learners with Restricted Mind Changes.
ALT 2005: 474-489 |
| 66 | EE | Steffen Lange,
Gunter Grieser,
Thomas Zeugmann:
Inductive inference of approximations for recursive concepts.
Theor. Comput. Sci. 348(1): 15-40 (2005) |
| 2004 |
|---|
| 65 | EE | John Case,
Sanjay Jain,
Rüdiger Reischuk,
Frank Stephan,
Thomas Zeugmann:
A Polynomial Time Learner for a Subclass of Regular Patterns
Electronic Colloquium on Computational Complexity (ECCC)(038): (2004) |
| 2003 |
|---|
| 64 | EE | Thomas Zeugmann:
Can Learning in the Limit Be Done Efficiently?
ALT 2003: 17-38 |
| 63 | EE | John Case,
Sanjay Jain,
Rüdiger Reischuk,
Frank Stephan,
Thomas Zeugmann:
Learning a Subclass of Regular Patterns in Polynomial Time.
ALT 2003: 234-246 |
| 62 | EE | Thomas Zeugmann:
Can Learning in the Limit Be Done Efficiently?
Discovery Science 2003: 46 |
| 61 | EE | Sanjay Jain,
Efim B. Kinber,
Rolf Wiehagen,
Thomas Zeugmann:
On learning of functions refutably.
Theor. Comput. Sci. 1(298): 111-143 (2003) |
| 2002 |
|---|
| 60 | | Frank Stephan,
Thomas Zeugmann:
Learning classes of approximations to non-recursive function.
Theor. Comput. Sci. 288(2): 309-341 (2002) |
| 2001 |
|---|
| 59 | | Naoki Abe,
Roni Khardon,
Thomas Zeugmann:
Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings
Springer 2001 |
| 58 | EE | Naoki Abe,
Roni Khardon,
Thomas Zeugmann:
Editors' Introduction.
ALT 2001: 1-8 |
| 57 | EE | Sanjay Jain,
Efim B. Kinber,
Rolf Wiehagen,
Thomas Zeugmann:
Learning Recursive Functions Refutably.
ALT 2001: 283-298 |
| 56 | EE | Thomas Zeugmann:
Stochastic Finite Learning.
SAGA 2001: 155-172 |
| 55 | | Peter Rossmanith,
Thomas Zeugmann:
Stochastic Finite Learning of the Pattern Languages.
Machine Learning 44(1/2): 67-91 (2001) |
| 54 | EE | Thomas Erlebach,
Peter Rossmanith,
Hans Stadtherr,
Angelika Steger,
Thomas Zeugmann:
Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries.
Theor. Comput. Sci. 261(1): 119-156 (2001) |
| 53 | EE | Rolf Wiehagen,
Thomas Zeugmann:
Foreword.
Theor. Comput. Sci. 268(2): 175-177 (2001) |
| 2000 |
|---|
| 52 | EE | Gunter Grieser,
Steffen Lange,
Thomas Zeugmann:
Learning Recursive Concepts with Anomalies.
ALT 2000: 101-115 |
| 51 | | Frank Stephan,
Thomas Zeugmann:
Average-Case Complexity of Learning Polynomials.
COLT 2000: 59-68 |
| 50 | | Rüdiger Reischuk,
Thomas Zeugmann:
An Average-Case Optimal One-Variable Pattern Language Learner.
J. Comput. Syst. Sci. 60(2): 302-335 (2000) |
| 49 | EE | Sanjay Jain,
Efim B. Kinber,
Steffen Lange,
Rolf Wiehagen,
Thomas Zeugmann:
Learning languages and functions by erasing.
Theor. Comput. Sci. 241(1-2): 143-189 (2000) |
| 1999 |
|---|
| 48 | EE | Frank Stephan,
Thomas Zeugmann:
On the Uniform Learnability of Approximations to Non-Recursive Functions.
ATL 1999: 276-290 |
| 47 | EE | Rüdiger Reischuk,
Thomas Zeugmann:
A Complete and Tight Average-Case Analysis of Learning Monomials.
STACS 1999: 414-423 |
| 46 | | John Case,
Sanjay Jain,
Steffen Lange,
Thomas Zeugmann:
Incremental Concept Learning for Bounded Data Mining.
Inf. Comput. 152(1): 74-110 (1999) |
| 1998 |
|---|
| 45 | | Michael M. Richter,
Carl H. Smith,
Rolf Wiehagen,
Thomas Zeugmann:
Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 8-10, 1998, Proceedings
Springer 1998 |
| 44 | EE | Michael M. Richter,
Carl H. Smith,
Rolf Wiehagen,
Thomas Zeugmann:
Editor's Introduction.
ALT 1998: 1-10 |
| 43 | EE | Rüdiger Reischuk,
Thomas Zeugmann:
Learning One-Variable Pattern Languages in Linear Average Time.
COLT 1998: 198-208 |
| 42 | EE | Peter Rossmanith,
Thomas Zeugmann:
Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data.
ICGI 1998: 13-24 |
| 41 | | Thomas Zeugmann:
Lange and Wiehagen's Pattern Language Learning Algorithm: An Average-Case Analysis with Respect to its Total Learning Time.
Ann. Math. Artif. Intell. 23(1-2): 117-145 (1998) |
| 40 | EE | Rüdiger Reischuk,
Thomas Zeugmann:
An Average-Case Optimal One-Variable Pattern Language Learner
Electronic Colloquium on Computational Complexity (ECCC) 5(69): (1998) |
| 1997 |
|---|
| 39 | | Thomas Erlebach,
Peter Rossmanith,
Hans Stadtherr,
Angelika Steger,
Thomas Zeugmann:
Learning One-Variable Pattern Languages Very Efficiently on Average, in Parallel, and by Asking Queries.
ALT 1997: 260-276 |
| 38 | | Carl H. Smith,
Rolf Wiehagen,
Thomas Zeugmann:
Classifying Predicates and Languages.
Int. J. Found. Comput. Sci. 8(1): 15- (1997) |
| 1996 |
|---|
| 37 | | Steffen Lange,
Rolf Wiehagen,
Thomas Zeugmann:
Learning by Erasing.
ALT 1996: 228-241 |
| 36 | | Rusins Freivalds,
Thomas Zeugmann:
Co-Learning of Recursive Languages from Positive Data.
Ershov Memorial Conference 1996: 122-133 |
| 35 | | Steffen Lange,
Thomas Zeugmann:
Incremental Learning from Positive Data.
J. Comput. Syst. Sci. 53(1): 88-103 (1996) |
| 34 | | Steffen Lange,
Thomas Zeugmann:
Set-Driven and Rearrangement-Independent Learning of Recursive Languages.
Mathematical Systems Theory 29(6): 599-634 (1996) |
| 33 | EE | Steffen Lange,
Thomas Zeugmann,
Shyam Kapur:
Monotonic and Dual Monotonic Language Learning.
Theor. Comput. Sci. 155(2): 365-410 (1996) |
| 1995 |
|---|
| 32 | | Klaus P. Jantke,
Takeshi Shinohara,
Thomas Zeugmann:
Algorithmic Learning Theory, 6th International Conference, ALT '95, Fukuoka, Japan, October 18-20, 1995, Proceedings
Springer 1995 |
| 31 | | Klaus P. Jantke,
Takeshi Shinohara,
Thomas Zeugmann:
Editor's Introduction.
ALT 1995: ix-xv |
| 30 | | Steffen Lange,
Thomas Zeugmann:
Trading monotonicity demands versus mind changes.
EuroCOLT 1995: 125-139 |
| 29 | | Rolf Wiehagen,
Thomas Zeugmann:
Learning and Consistency.
GOSLER Final Report 1995: 1-24 |
| 28 | | Rolf Wiehagen,
Carl H. Smith,
Thomas Zeugmann:
Classifying Recursive Predicates and Languages.
GOSLER Final Report 1995: 174-189 |
| 27 | | Thomas Zeugmann,
Steffen Lange:
A Guided Tour Across the Boundaries of Learning Recursive Languages.
GOSLER Final Report 1995: 190-258 |
| 26 | | William I. Gasarch,
Efim B. Kinber,
Mark G. Pleszkoch,
Carl H. Smith,
Thomas Zeugmann:
Learning via Queries with Teams and Anomalies.
Fundam. Inform. 23(1): 67-89 (1995) |
| 25 | | Thomas Zeugmann,
Steffen Lange,
Shyam Kapur:
Characterizations of Monotonic and Dual Monotonic Language Learning
Inf. Comput. 120(2): 155-173 (1995) |
| 1994 |
|---|
| 24 | | Steffen Lange,
Thomas Zeugmann:
Set-Driven and Rearrangement-Independent Learning of Recursive Languages.
AII/ALT 1994: 453-468 |
| 23 | | Thomas Zeugmann:
Average Case Analysis of Pattern Language Learning Algorithms (Abstract).
AII/ALT 1994: 8-9 |
| 22 | | Rolf Wiehagen,
Thomas Zeugmann:
Ignoring data may be the only way to learn efficiently.
J. Exp. Theor. Artif. Intell. 6: 131-144 (1994) |
| 1993 |
|---|
| 21 | EE | Steffen Lange,
Thomas Zeugmann:
Language Learning in Dependence on the Space of Hypotheses.
COLT 1993: 127-136 |
| 20 | | Steffen Lange,
Thomas Zeugmann:
Language Learning with a Bounded Number of Mind Changes.
STACS 1993: 682-691 |
| 19 | | Steffen Lange,
Thomas Zeugmann:
Learning Recursive Languages with Bounded Mind Changes.
Int. J. Found. Comput. Sci. 4(2): 157-178 (1993) |
| 1992 |
|---|
| 18 | | Steffen Lange,
Thomas Zeugmann:
A Unifying Approach to Monotonic Language Learning on Informant.
AII 1992: 244-259 |
| 17 | | Rolf Wiehagen,
Thomas Zeugmann:
Too Much Can be Too Much for Learning Efficiently.
AII 1992: 72-86 |
| 16 | EE | Steffen Lange,
Thomas Zeugmann:
Types of Monotonic Language Learning and Their Characterization.
COLT 1992: 377-390 |
| 15 | | Thomas Zeugmann:
Highly Parallel Computations Modulo a Number Having Only Small Prime Factors
Inf. Comput. 96(1): 95-114 (1992) |
| 1991 |
|---|
| 14 | | Steffen Lange,
Thomas Zeugmann:
Monotonic Versus Nonmonotonic Language Learning.
Nonmonotonic and Inductive Logic 1991: 254-269 |
| 13 | | Efim B. Kinber,
Thomas Zeugmann:
One-Sided Error Probabilistic Inductive Inference and Reliable Frequency Identification
Inf. Comput. 92(2): 253-284 (1991) |
| 1990 |
|---|
| 12 | EE | Efim B. Kinber,
William I. Gasarch,
Thomas Zeugmann,
Mark G. Pleszkoch,
Carl H. Smith:
Learning Via Queries With Teams and Anomilies.
COLT 1990: 327-337 |
| 11 | | Thomas Zeugmann:
Computing Large Polynomial Powers Very Fast in Parallel.
MFCS 1990: 538-544 |
| 10 | | Thomas Zeugmann:
Inductive Inference of Optimal Programs: A Survey and Open Problems.
Nonmonotonic and Inductive Logic 1990: 208-222 |
| 1989 |
|---|
| 9 | | Efim B. Kinber,
Thomas Zeugmann:
Refined Query Inference (Extended Abstract).
AII 1989: 148-160 |
| 8 | | Efim B. Kinber,
Thomas Zeugmann:
Monte-Carlo Inference and Its Relations to Reliable Frequency Identification.
FCT 1989: 257-266 |
| 7 | | Thomas Zeugmann:
Improved Parallel Computations in the Ring Z/palpha.
Elektronische Informationsverarbeitung und Kybernetik 25(10): 543-547 (1989) |
| 1988 |
|---|
| 6 | | Thomas Zeugmann:
On the Power of Recursive Optimizers.
Theor. Comput. Sci. 62(3): 289-310 (1988) |
| 1986 |
|---|
| 5 | | Thomas Zeugmann:
On Barzdin's Conjecture.
AII 1986: 220-227 |
| 1985 |
|---|
| 4 | | Thomas Zeugmann:
On recursive optimizers.
Mathematical Methods of Specification and Synthesis of Software Systems 1985: 240-245 |
| 3 | | Efim B. Kinber,
Thomas Zeugmann:
Inductive Inference of Almost Everywhere Correct Programs by Reliably Working Strategies.
Elektronische Informationsverarbeitung und Kybernetik 21(3): 91-100 (1985) |
| 1983 |
|---|
| 2 | | Thomas Zeugmann:
A-posteriori Characterizations in Inductive Inference of Recursive Functions.
Elektronische Informationsverarbeitung und Kybernetik 19(10/11): 559-594 (1983) |
| 1 | | Thomas Zeugmann:
On the Synthesis of Fastest Programs in Inductive Inference.
Elektronische Informationsverarbeitung und Kybernetik 19(12): 625-642 (1983) |
