J. Arima and H. Sawamura | |
|
Reformulation of Explanation by Linear Logic - Toward Logic for
Explanation
| | 45 - 57
|
J. Barzdins, G. Barzdins, K. Apsitis, U. Sarkans | |
|
Towards Efficient Inductive Synthesis of Expressions from
Input/Output Examples
| | 59 - 72
|
M. Hagiya | |
|
A Typed lambda-Calculus for Proving-by-Example and Bottom-up
Generalization Procedure
| | 73 - 86
|
K.P. Jantke and S. Lange | |
|
Case-Based Representation and Learning Pattern Languages
| | 87 - 100
|
T. Sato and S. Akiba | |
|
Inductive Resolution
| | 101 - 110
|
A. Yamamoto | |
|
Generalized Unification as Background Knowledge in Learning Logic
Programs
| | 111 - 122
|
Inductive Inference | |
|
Y. Mukouchi and S. Arikawa | |
|
Inductive Inference Machines That Can Refute Hypothesis Spaces
| | 123 - 136
|
R. Freivalds and C.H. Smith | |
|
On the Duality between Mechanistic Learners and What it is They Learn
| | 137 - 149
|
S. Jain and A. Sharma | |
|
On Aggregating Teams of Learning Machines
| | 150 - 163
|
J. Viksna | |
|
Learning with Growing Quality
| | 164 - 172
|
R. Daley and B. Kalyanasundaram | |
|
Use of Reduction Arguments on Determining Popperian FIN-Type Learning
Capabilities
| | 173 - 186
|
T. Moriyama and M. Sato | |
|
Properties of Language Classes with Finite Elasticity
| | 187 - 196
|
S. Kapur | |
|
Uniform Characterizations of Various Kinds of Language Learning
| | 197 - 208
|
T. Knuutila | |
|
How to Invent Characterizable Inference Methods for Regular Languages
| | 209 - 222
|
Approximate Learning | |
|
J.R. Cuellar and H.U. Simon | |
|
Neural Discriminant Analysis
| | 223 - 236
|
M. Iwayama, N. Indurkhya and H. Motoda | |
|
A New Algorithm for Automatic Configuration of Hidden Markov Models
| | 237 - 250
|
A. Sakurai | |
|
On the VC-dimension of Depth Four Threshold Circuits and the
Comlexity of Boolean-valued Functions
| | 251 - 264
|
E. Takimoto and A. Maruoka | |
|
On the Sample Complexity of Consistent Learning with One-Sided
Error
| | 265 - 278
|
A. Shinohara | |
|
Complexity of Computing Vapnik-Chervonenkis Dimension
| | 279 - 287
|
S. Hasegawa, H. Imai and M. Ishiguro | |
|
epsilon-approximations of k-label spaces
| | 288 - 299
|
Query Learning | |
|
A. Nakamura and N. Abe | |
|
Exact Learning of Linear Combinations of Monotone Terms from
Function Value Queries
| | 300 - 313
|
R. Siromoney, D.G. Thomas, K.G. Subramanian, V.R. Dare | |
|
Thue Systems and DNA - A Learning Algorithm for a Subclass
| | 314 - 327
|
Y. Ishigami and S. Tani | |
|
The VC-dimensions of Finite Automata with n states
| | 328 - 341
|
Explanation-Based Learning
| |
|
K. Yoshida, H. Motoda, and N. Indurkhya | |
|
Unifying Learning Methods by Colored Digraphs
| | 342 - 355
|
M. Suwa and H. Motoda | |
|
A Perceptual Criterion for Visually Controlling Learning
| | 356 - 369
|
S. Kobayashi | |
|
Learning Strategies Using Decision Lists
| | 370 - 383
|
New Learning Paradigms | |
|
N. Zhong and S. Ohsuga | |
|
A Decomposition Based Induction Model for Discovering Concept
Clusters from Databases
| | 384 - 397
|
J.G. Ganascia | |
|
Algebraic Structure of Some Learning Systems
| | 398 - 409
|
S. Tsumoto and H. Tanaka | |
|
Induction of Probabilistic Rules Based on Rough Set Theory
| | 410 - 423
|
Index of Authors | | 424
|