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Artificial Intelligence Frontiers In Statistics / Edition 1

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Overview

This book presents a summary of recent work on the interface between artificial intelligence and statistics. It does this through a series of papers by different authors working in different areas of this interface. These papers are a selected and referenced subset of papers presented at the 3rd Interntional Workshop on Artificial Intelligence and Statistics, Florida, January 1991.

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Product Details

  • ISBN-13: 9780412407109
  • Publisher: Taylor & Francis
  • Publication date: 12/1/1992
  • Edition description: 1st ed
  • Edition number: 1
  • Pages: 432
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.94 (d)

Table of Contents

List of contributors
Introduction
Pt. 1 Statistical expert systems 1
1 DEXPERT: an expert system for the design of experiments 3
2 Inside two commercially available statistical expert systems 17
3 AMIA: Aide a la Modelisation par l'Intelligence Artificielle (expert system for simulation modelling and sectoral forecasting) 31
4 An architecture for knowledge-based statistical support systems 39
5 Enhancing explanation capabilities of statistical expert systems through hypertext 46
6 Measurement scales as metadata 54
Pt. 2 Belief networks 65
7 On the design of belief networks for knowledge-based systems 67
8 Lack-of-information based control in graphical belief systems 82
9 Adaptive importance sampling for Bayesian networks applied to filtering problems 90
10 Intelligent arc addition, belief propagation and utilization of parallel processors by probabilistic inference engines 106
11 A new method for representing and solving Bayesian decision problems 109
Pt. 3 Learning 139
12 Inferring causal structure in mixed populations 141
13 A knowledge acquisition inductive system guided by empirical interpretation of derived results 156
14 Incorporating statistical techniques into empirical symbolic learning systems 168
15 Learning classification trees 182
16 An analysis of two probabilistic model induction techniques 202
Pt. 4 Neural networks 215
17 A robust back propagation algorithm for function approximation 217
18 Maximum likelihood training of neural networks 241
19 A connectionist knowledge acquisition tool: CONKAT 256
20 Connectionist, rule-based, and Bayesian decision aids: an empirical comparison 264
Pt. 5 Text manipulation 279
21 Statistical approaches to aligning sentences and identifying word correspondences in parallel texts: a report on work in progress 281
22 Probabilistic text understanding 295
23 The application of machine learning techniques in subject classification 312
Pt. 6 Other areas 325
24 A statistical semantics for causation 327
25 Admissible stochastic complexity models for classification problems 335
26 Combining the probability judgements of experts: statistical and artificial intelligence approaches 348
27 Randomness and independence in non-monotonic reasoning 362
28 Consistent regions in probabilistic logic when using different norms 370
29 A decision theoretic approach to controlling the cost of planning 387
Index 401
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