Selecting Models from Data

Artificial Intelligence and Statistics IV
Herausgegeben von Cheeseman, P.; Oldford, R.W.

• Broschiertes Buch

Produktbeschreibung

This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

Produktdetails

• Lecture Notes in Statistics .89
• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-0-387-94281-0
• 1994.
• Seitenzahl: 504
• Erscheinungstermin: 27. Mai 1994
• Englisch
• Abmessung: 235mm x 155mm x 28mm
• Gewicht: 755g
• ISBN-13: 9780387942810
• ISBN-10: 0387942815
• Artikelnr.: 24461961

Inhaltsangabe

I Overviews: Model Selection.
1 Statistical strategy: step 1.
2 Rational Learning: Finding a Balance Between Utility and Efficiency.
3 A new criterion for selecting models from partially observed data.
4 Small
sample and large
sample statistical model selection criteria.
5 On the choice of penalty term in generalized FPE criterion.
6 Cross
Validation, Stacking and Bi
Level Stacking: Meta
Methods for Classification Learning.
7 Probabilistic approach to model selection: comparison with unstructured data set.
8 Detecting and Explaining Dependencies in Execution Traces.
9 A method for the dynamic selection of models under time constraints.
II Graphical Models.
10 Strategies for Graphical Model Selection.
11 Conditional dependence in probabilistic networks.
12 Reuse and sharing of graphical belief network components.
13 Bayesian Graphical Models for Predicting Errors in Databases.
14 Model Selection for Diagnosis and Treatment Using Temporal Influence Diagrams.
15 Diagnostic systems by model selection: a case study.
16 A Survey of Sampling Methods for Inference on Directed Graphs.
17 Minimizing decision table sizes in influence diagrams: dimension shrinking.
18 Models from Data for Various Types of Reasoning.
III Causal Models.
19 Causal inference in artificial intelligence.
20 Inferring causal structure among unmeasured variables.
21 When can association graphs admit a causal interpretation?.
22 Inference, Intervention, and Prediction.
23 Attitude Formation Models: Insights from TETRAD.
24 Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods.
25 Path Analysis Models of an Autonomous Agent in a Complex Environment.
IV Particular Models.
26 A Parallel Constructor of Markov Networks.
27 Capturing observations in a nonstationary hidden Markov model.
28 Extrapolating Definite Integral Information.
29 The Software Reliability Consultant.
30 Statistical Reasoning to Enhance User Modelling in Consulting Systems.
31 Selecting a frailty model for longitudinal breast cancer data.
32 Optimal design of reflective sensors using probabilistic analysis.
V Similarity
Based Models.
33 Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks.
34 Dynamic Recursive Model Class Selection for Classifier Construction.
35 Minimizing the expected costs of classifying patterns by sequential costly inspections.
36 Combining a knowledge
based system and a clustering method for a construction of models in ill
structured domains.
37 Clustering of Symbolically Described Events for Prediction of Numeric Attributes.
38 Symbolic Classifiers: Conditions to Have Good Accuracy Performance.
VI Regression and Other Statistical Models.
39 Statistical and neural network techniques for nonparametric regression.
40 Multicollinearity: A tale of two nonparametric regressions.
41 Choice of Order in Regression Strategy.
42 Modelling response models in software.
43 Principal components and model selection.
VII Algorithms and Tools.
44 Algorithmic speedups in growing classification trees by using an additive split criterion.
45 Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems.
46 Simulated annealing in the construction of near
optimal decision trees.
47 SA/GA: Survival of the Fittest in Alaska.
48 A Tool for Model Generation and Knowledge Acquisition.
49 Using knowledge
assisted discriminant analysis to generate new comparative terms.