SESSION 1
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M. Blum | |
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Turning PAC learning into PERFECT learning (Invited Lecture)
| | 1 - 1
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SESSION 2
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A. Ambainis,
K. Apsitis,
R. Freivalds,
W. Gasarch, and
C.H. Smith | |
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Team learning as a game
| | 2 - 17
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E. Hirowatari and
S. Arikawa
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Inferability of recursive real-valued functions
| | 18 - 31
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S. Jain,
S. Lange, and J. Nessel | |
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Learning of r.e. languages from good examples
| | 32 - 47
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S. Kobayashi and
T. Yokomori | |
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Identifiability of subspaces and homomorphic images of
zero-reversible languages
| | 48 - 61
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SESSION 3 | |
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L. Pitt | |
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On exploiting knowledge and concept use in learning theory
(invited lecture)
| | 62 - 84
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SESSION 4 | |
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C. Domingo, T. Tsukiji, and
O. Watanabe | |
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Partial Occam's razor and its applications
| | 85 - 99
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M. Sitharam and
T. Straney | |
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Derandomized learning of Boolean functions
| | 100 - 115
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R. Parekh and
V. Honavar | |
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Learning DFA from simple examples
| | 116 - 131
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SESSION 5 | |
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F. Denis and
R. Gilleron | |
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PAC learning under helpful distributions
| | 132 - 145
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H. Qiao, N.S.V. Rao, and V. Protopopescu | |
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PAC learning using Nadaraya-Watson estimator based on
orthonormal systems
| | 146 - 160
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E. Boros, T. Ibaraki, and K. Makino | |
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Monotone extensions of Boolean data sets
| | 161 - 175
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SESSION 6 | |
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M. Sato | |
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Classical Brouwer-Heyting-Kolmogorov interpretation (invited lecture)
| | 176 - 196
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SESSION 7 | |
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Y. Mukouchi | |
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Inferring a system from examples with time passage
| | 197 - 211
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S. Matsumoto, Y. Hayashi, and
T. Shoudai | |
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Polynomial time inductive inference of regular term tree languages
from positive data
| | 212 - 227
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J. Case,
S. Jain, and
A. Sharma | |
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Synthesizing noise-tolerant language learners
| | 228 - 243
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A. Ambainis,
K. Apsitis,
C. Calude,
R. Freivalds,
M. Karpinski,
T. Larfeldt, I. Sala,
and J. Smotrovs | |
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Effects of Kolmogorov complexity present in inductive inference as
well
| | 244 - 259
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SESSION 8 | |
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T. Erlebach,
P. Rossmanith,
H. Stadtherr,
A. Steger, and
T. Zeugmann | |
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Learning one-variable pattern languages very efficiently on average,
in parallel, and by asking queries
Abstract.
| | 260 - 276
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J. Köbler
and
W. Lindner
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Oracles in $\Sigma_2^p$ are sufficient for exact learning
| | 277 - 290
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V. Arvind and N.,V. Vinodchandran | |
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Exact learning via teaching assistants
| | 291 - 306
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A. Nakamura | |
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An efficient exact learning algorithm for ordered binary decision
diagrams
| | 307 - 322
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SESSION 9 | |
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V. Vovk | |
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Probability theory for the Brier game
| | 323 - 338
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X. Zhang and
M. Numao
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Learning and revising theories in noisy domains
| | 339 - 351
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L. Gurvits | |
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A note on a scale-sensitive dimension of linear bounded
functionals in Banach spaces
| | 352 - 363
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SESSION 10 | |
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W. Maass | |
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On the relevance of time in neural computation and learning (invited
lecture)
| | 364 - 384
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SESSION 11 | |
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E. Takimoto,
K. Hirai, and
A. Maruoka
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A simple algorithm for predicting nearly as well as the best
pruning labeled
with the best prediction values of a decision tree
| | 385 - 400
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S. Kwek | |
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Learning disjunctions of features
| | 401 - 415
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H. Sakamoto | |
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Learning simple deterministic finite-memory automata
| | 416 - 431
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SESSION 12 | |
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H. Arimura | |
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Learning acyclic first-order Horn sentences from entailment
| | 432 - 445
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T. Hegedüs and
P. Indyk | |
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On learning disjunctions of zero-one threshold functions with queries
| | 446 - 460
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Author Index | |
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| | 461
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