Learning from Data: Artificial Intelligence and Statistics V

ISBN-13: 9780387947365 / Angielski / Miękka / 1996 / 450 str.

Doug Fisher; Hans-J Lenz; Douglas H. Fisher

Opis

This volume contains a revised collection of papers presented at the Fifth International Workshop on Artificial Intelligence and Statistics in 1995. The topics covered includenatural language applications, causality and graphical models, classification, learning, and knowledge discovery.

Spis treści

I Causality.- 1 Two Algorithms for Inducing Structural Equation Models from Data.- 2 Using Causal Knowledge to Learn More Useful Decision Rules from Data.- 3 A Causal Calculus for Statistical Research.- 4 Likelihood-based Causal Inference.- II Inference and Decision Making.- 5 Ploxoma: Testbed for Uncertain Inference.- 6 Solving Influence Diagrams Using Gibbs Sampling.- 7 Modeling and Monitoring Dynamic Systems by Chain Graphs.- 8 Propagation of Gaussian Belief Functions.- 9 On Test Selection Strategies for Belief Networks.- 10 Representing and Solving Asymmetric Decision Problems Using Valuation Networks.- 11 A Hill-Climbing Approach for Optimizing Classification Trees.- III Search Control in Model Hunting.- 12 Learning Bayesian Networks is NP-Complete.- 13 Heuristic Search for Model Structure: The Benefits of Restraining Greed.- 14 Learning Possibilistic Networks from Data.- 15 Detecting Imperfect Patterns in Event Streams Using Local Search.- 16 Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms.- 17 An Axiomatization of Loglinear Models with an Application to the Model-Search Problem.- 18 Detecting Complex Dependencies in Categorical Data.- IV Classification.- 19 A Comparative Evaluation of Sequential Feature Selection Algorithms.- 20 Classification Using Bayes Averaging of Multiple, Relational Rule-Based Models.- 21 Picking the Best Expert from a Sequence.- 22 Hierarchical Clustering of Composite Objects with a Variable Number of Components.- 23 Searching for Dependencies in Bayesian Classifiers.- V General Learning Issues.- 24 Statistical Analysis fo Complex Systems in Biomedicine.- 25 Learning in Hybrid Noise Environments Using Statistical Queries.- 26 On the Statistical Comparison of Inductive Learning Methods.- 27 Dynamical Selection of Learning Algorithms.- 28 Learning Bayesian Networks Using Feature Selection.- 29 Data Representations in Learning.- VI EDA: Tools and Methods.- 30 Rule Induction as Exploratory Data Analysis.- 31 Non-Linear Dimensionality Reduction: A Comparative Performance Analysis.- 32 Omega-Stat: An Environment for Implementing Intelligent Modeling Strategies.- 33 Framework for a Generic Knowledge Discovery Toolkit.- 34 Control Representation in an EDA Assistant.- VII Decision and Regression Tree Induction.- 35 A Further Comparison of Simplification Methods for Decision-Tree Induction.- 36 Robust Linear Discriminant Trees.- 37 Tree Structured Interpretable Regression.- 38 An Exact Probability Metric for Decision Tree Splitting.- VIII Natural Language Processing.- 39 Two Applications of Statistical Modelling to Natural Language Processing.- 40 A Model for Part-of-Speech Prediction.- 41 Viewpoint-Based Measurement of Semantic Similarity Between Words.- 42 Part-of-Speech Tagging from “Small” Data Sets.