## 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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