## Artificial Intelligence Frontiers in Statistics: Al and Statistics IIIThis 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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### Contents

an expert system for the design of experiments | 3 |

Inside two commercially available statistical expert systems | 17 |

Aide à la Modélisation par lIntelligence Artificielle | 31 |

An architecture for knowledgebased statistical support systems | 39 |

Enhancing explanation capabilities of statistical expert systems | 46 |

Measurement scales as metadata | 54 |

On the design of belief networks for knowledgebased systems | 67 |

Lackofinformation based control in graphical belief systems | 82 |

An analysis of two probabilistic model induction techniques | 202 |

A robust back propagation algorithm for function approximation | 217 |

Maximum likelihood training of neural networks | 241 |

CONKAT | 256 |

Statistical approaches to aligning sentences and identifying word | 281 |

Probabilistic text understanding | 295 |

The application of machine learning techniques in subject | 312 |

A statistical semantics for causation | 327 |

Intelligent arc addition belief propagation and utilization | 106 |

Inferring causal structure in mixed populations | 141 |

A knowledge acquisition inductive system guided | 156 |

Incorporating statistical techniques into empirical symbolic | 168 |

Learning classification trees | 182 |

### Other editions - View all

Artificial Intelligence Frontiers in Statistics: AI and statistics III D. J. Hand No preview available - 2013 |

### Common terms and phrases

1993 by Chapman agent algorithm alignment analysis application approach approximation Artificial Intelligence Artificial Intelligence Frontiers attributes auxiliary network Bayesian belief function belief networks BP algorithm CART causal structure class probability classification complexity components computational conditional entropy conditional independence conditional probability CONKAT connectionist CONSTRUCTOR D.J. Hand decision problem decision tree defined denote described diagnosis directed acyclic graph domain knowledge Equation error rate estimate EU-strategy evaluation example expected utility facet terms factors Frontiers in Statistics given graph hidden layer impropriety inductive inference influence diagrams input iteration KBFE knowledge-based leaf linear Machine Learning matching measure metadata method minimal neural networks node observations optimal option trees output parameters possible posterior probability prior probabilistic random variables representation represented rules sample selection sentences sequence Shenoy specific statistical expert systems strategy subset techniques Theorem theory training data training set utility valuation values vector weights

### References to this book

Learning from Data: Artificial Intelligence and Statistics V Doug Fisher,Hans-J. Lenz,Hans-J Lenz Limited preview - 1996 |