Table of Contens
Table of Contens |
|
| S. Amari | ||
| Mathematical Theory of Neural Learning | 3 - 20 | |
| D. Haussler | ||
| Decision Theoretic Generalization of the PAC Learning Model | 21 - 41 | |
| S. Muggleton | ||
| Inductive Logic Programming | 42 - 62 |
Neural Networks
| A. Namatame | ||
| Structured Neural Networks and their Flash Learning | 67 - 80 | |
| R. Oka | ||
| A Self-Organizing Network Composed of Symbol Nodes with Location Parameter | 81 - 94 | |
| Concept Formation and Recognition | ||
| B. Shekar, M. Narasimha Murty and G. Krishna | ||
| The Function-Acquisition Paradigm in a Knowledge-Based Concept-Synthesis Environment | 97 - 108 | |
| V. Gusev and N. Chuzhanova | ||
| The Algorithms of Recognition of the Functional Sites in Genetic Texts | 109 - 119 | |
| T. Unemi | ||
| On Inductive Learning for Three Kinds of Data Structures | 120 - 133 | |
| R. Orihara, A. Osuga and Y. Kusui | ||
| On Paraphrasing-Based Analogical Reasoning - as a Theoretical Base of the Abduction Support System | 134 - 148 | |
| Analogical Reasoning | ||
| M. Harao | ||
| Analogical Reasoning Based on Higher-Order Unification | 151 - 163 | |
| J. Arima | ||
| Analogy by Simulation - A Weak Justification Method | 164 - 173 | |
| B. Indurkhya | ||
| On the Role of Interpretive Analogy in Learning | 174 - 189 | |
| Approximate Learning | ||
| Y. Sakakibara | ||
| Occam Algorithms for Learning from Noisy Examples | 193 - 208 | |
| J. Kivinen | ||
| Reliable and Useful Learning with Uniform Probability Distributions | 209 - 222 | |
| N. Abe | ||
| Learning Commutative Deterministic Finite State Automata in Polynomial Time | 223 - 235 | |
| N. Cesa-Bianchi | ||
| Learning the Distribution in the Extended PAC Model | 236 - 246 | |
| A. Shinohara and S. Miyano | ||
| Teachability in Computational Learning | 247 - 255 | |
| M. Fulk and S. Jain | ||
| Approximate Inference and Scientific Method | 256 - 265 | |
| Inductive Inference | ||
| K.P. Jantke | ||
| Monotonic and Non-Monotonic Inductive Inference | 269 - 281 | |
| J. Case, S. Jain and A. Sharma | ||
| Anomalous Learning Helps Succinctness | 282 - 288 | |
| S. Lange and R. Wiehagen | ||
| Polynomial-Time Inference of Pattern Languages | 289 - 301 | |
| Y. Takada | ||
| Learning Equal Matrix Grammars and Multiple Automata with Structural Information | 302 - 313 | |
| Y. Takada, K. Hiraishi and Y. Sakakibara | ||
| Exact Learning of Semilinear Sets | 314 - 324 | |
| P. Garcia, E. Vidal and J. Oncina | ||
| Learning Locally Testable Languages in the Strict Sense | 325 - 338 | |
| T. Shinohara | ||
| Inductive Inference of Monotonic Formal Systems from Positive Data | 339 - 351 | |
| New Learning Paradigms | ||
| S. Liu and M. Hagiya | ||
| Model Inference of Constraint Recursive Figures | 355 - 367 | |
| S. Muggleton and C. Feng | ||
| Efficient Induction of Logic Programs | 368 - 381 | |
| T. Tanaka | ||
| Deciding What to Learn in Explanation-Based Macro Rule Learning | 382 - 395 | |
| M. Hagiya | ||
| Synthesis of Rewrite Programs by Higher-Order and Semantic Unification | 396 - 410 | |
| A. Togashi and S. Noguchi | ||
| Inductive Inference of Term Rewriting Systems Realizing Algebras | 411 - 424 | |
| P. Laird and E. Gamble | ||
| EBG and Term Rewriting Systems | 425 - 440 | |
| Index of Authors | 441 |
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