Artificial Intelligence Frontiers In Statistics / Edition 1

Artificial Intelligence Frontiers In Statistics / Edition 1

by David J. Hand
     
 

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,

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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.

Product Details

ISBN-13:
9780412407109
Publisher:
Taylor & Francis
Publication date:
12/01/1992
Edition description:
1st ed
Pages:
432
Product dimensions:
6.14(w) x 9.21(h) x 0.94(d)
Age Range:
18 Years

Table of Contents

List of contributors
Introduction
Pt. 1Statistical expert systems1
1DEXPERT: an expert system for the design of experiments3
2Inside two commercially available statistical expert systems17
3AMIA: Aide a la Modelisation par l'Intelligence Artificielle (expert system for simulation modelling and sectoral forecasting)31
4An architecture for knowledge-based statistical support systems39
5Enhancing explanation capabilities of statistical expert systems through hypertext46
6Measurement scales as metadata54
Pt. 2Belief networks65
7On the design of belief networks for knowledge-based systems67
8Lack-of-information based control in graphical belief systems82
9Adaptive importance sampling for Bayesian networks applied to filtering problems90
10Intelligent arc addition, belief propagation and utilization of parallel processors by probabilistic inference engines106
11A new method for representing and solving Bayesian decision problems109
Pt. 3Learning139
12Inferring causal structure in mixed populations141
13A knowledge acquisition inductive system guided by empirical interpretation of derived results156
14Incorporating statistical techniques into empirical symbolic learning systems168
15Learning classification trees182
16An analysis of two probabilistic model induction techniques202
Pt. 4Neural networks215
17A robust back propagation algorithm for function approximation217
18Maximum likelihood training of neural networks241
19A connectionist knowledge acquisition tool: CONKAT256
20Connectionist, rule-based, and Bayesian decision aids: an empirical comparison264
Pt. 5Text manipulation279
21Statistical approaches to aligning sentences and identifying word correspondences in parallel texts: a report on work in progress281
22Probabilistic text understanding295
23The application of machine learning techniques in subject classification312
Pt. 6Other areas325
24A statistical semantics for causation327
25Admissible stochastic complexity models for classification problems335
26Combining the probability judgements of experts: statistical and artificial intelligence approaches348
27Randomness and independence in non-monotonic reasoning362
28Consistent regions in probabilistic logic when using different norms370
29A decision theoretic approach to controlling the cost of planning387
Index401

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