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From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data

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The book aims to propose a theoretical and applicatory framework for extracting formal rules from data. To this end recent approaches in relevant disciplines are examined that bring together two typical goals of conventional Artificial Intelligence and connectionism - respectively, deducing within an axiomatic shell formal rules about a phenomenon and inferring the actual behavior of it from examples - into a challenging inferential framework where we learn from data and understand what we have learned. The goal ...

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Overview

The book aims to propose a theoretical and applicatory framework for extracting formal rules from data. To this end recent approaches in relevant disciplines are examined that bring together two typical goals of conventional Artificial Intelligence and connectionism - respectively, deducing within an axiomatic shell formal rules about a phenomenon and inferring the actual behavior of it from examples - into a challenging inferential framework where we learn from data and understand what we have learned. The goal is to obtain a translation of the subsymbolic structure of the data - stored in the synapses of a neural network - into formal properties described by rules.
To capture this journey from synapses to rules and then render it manageable for real world learning tasks, the contributions deal in depth with the following aspects: i. theoretical foundations of learning algorithms and soft computing; ii. intimate relationships between symbolic and subsymbolic reasoning methods; iii. integration of the related hosting architectures in both physiological and artificial brain.

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Product Details

  • ISBN-13: 9780306474026
  • Publisher: Springer US
  • Publication date: 10/28/2002
  • Edition description: 2002
  • Edition number: 1
  • Pages: 410

Table of Contents

I The Theoretical Bases of Learning 1
1 The Statistical Bases on Learning 5
2 PAC Meditation on Boolean Formulas 41
3 Learning Regression Functions 61
4 Cooperative Games in a Stochastic Environment 75
5 If-Then-Else and Rule Extraction from Two Sets of Rules 87
6 Extracting Interpretable Fuzzy Knowledge from Data 109
7 Fuzzy Methods for Simplifying a Boolean Formula Inferred from Examples 117
II Physical Aspects of Learning 135
8 On Mapping and Maps in the Central Nervous System 139
9 Molecular Basis of Learning and Memory: Modelling Based on Receptor Mosaics 165
10 Physiological and Logical Brain Functionalities: A Hypothesis for a Self-Referential Brain Activity 197
11 Modeling of Spontaneous Bursting Activity Observed in In-Vitro Neural Networks 219
12 The Importance of Data for Training Intelligent Devices 229
13 Learning and Checking Confidence Regions for the Hazard Function of Biomedical Data 251
III Systems that Bridge the Gap 273
14 Integrating Symbol-Oriented and Sub-Symbolic Reasoning Methods into Hybrid Systems 275
15 From the Unconscious to the Conscious 293
16 On Neural Networks, Connectionism and Brain-Like Learning 315
17 Adaptive Computation in Data Structures and Webs 343
18 IUANT: An Updating Method for Supervised Neural Structures 363
Conclusions 371
References 372
Index 385
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