Causal models and intelligent data management
著者
書誌事項
Causal models and intelligent data management
Springer, c1999
- :hardcover : alk. pap
大学図書館所蔵 全6件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.
目次
I. Causal Models.- 1. Statistics, Causality, and Graphs.- 1.1 A Century of Denial.- 1.2 Researchers in Search of a Language.- 1.3 Graphs as a Mathematical Language.- 1.4 The Challenge.- References.- 2. Causal Conjecture.- 2.1 Introduction.- 2.2 Variables in a Probability Tree.- 2.3 Causal Uncorrelatedness.- 2.4 Three Positive Causal Relations.- 2.5 Linear Sign.- 2.6 Causal Uncorrelatedness Again.- 2.7 Scored Sign.- 2.8 Tracking.- References.- 3. Who Needs Counterfactuals?.- 3.1 Introduction.- 3.1.1 Decision-Theoretic Framework.- 3.1.2 Unresponsiveness and Insensitivity.- 3.2 Counterfactuals.- 3.3 Problems of Causal Inference.- 3.3.1 Causes of Effects.- 3.3.2 Effects of Causes.- 3.4 The Counterfactual Approach.- 3.4.1 The Counterfactual Setting.- 3.4.2 Counterfactual Assumptions.- 3.5 Homogeneous Population.- 3.5.1 Experiment and Inference.- 3.6 Decision-Analytic Approach.- 3.7 Sheep and Goats.- 3.7.1 ACE.- 3.7.2 Neyman and Fisher.- 3.7.3 Bioequivalence.- 3.8 Causes of Effects.- 3.8.1 A Different Approach?.- 3.9 Conclusion.- References.- 4. Causality: Independence and Determinism.- 4.1 Introduction.- 4.2 Conclusion.- References.- II. Intelligent Data Management.- 5. Intelligent Data Analysis and Deep Understanding.- 5.1 Introduction.- 5.2 The Question: The Strategy.- 5.3 Diminishing Returns.- 5.4 Conclusion.- References.- 6. Learning Algorithms in High Dimensional Spaces.- 6.1 Introduction.- 6.2 SVM for Pattern Recognition.- 6.2.1 Dual Representation of Pattern Recognition.- 6.3 SVM for Regression Estimation.- 6.3.1 Dual Representation of Regression Estimation.- 6.3.2 SVM Applet and Software.- 6.4 Ridge Regression and Least Squares Methods in Dual Variables.- 6.5 Transduction.- 6.6 Conclusion.- References.- 7. Learning Linear Causal Models by MML Sampling.- 7.1 Introduction.- 7.2 Minimum Message Length Principle.- 7.3 The Model Space.- 7.4 The Message Format.- 7.5 Equivalence Sets.- 7.5.1 Small Effects.- 7.5.2 Partial Order Equivalence.- 7.5.3 Structural Equivalence.- 7.5.4 Explanation Length.- 7.6 Finding Good Models.- 7.7 Sampling Control.- 7.8 By-products.- 7.9 Prior Constraints.- 7.10 Test Results.- 7.11 Remarks on Equivalence.- 7.11.1 Small Effect Equivalence.- 7.11.2 Equivalence and Causality.- 7.12 Conclusion.- References.- 8. Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis.- References.- 9. On the Accuracy of Stochastic Complexity Approximations.- 9.1 Introduction.- 9.2 Stochastic Complexity and Its Applications.- 9.3 Approximating the Stochastic Complexity in the Incomplete Data Case.- 9.4 Empirical Results.- 9.4.1 The Problem.- 9.4.2 The Experimental Setting.- 9.4.3 The Algorithms.- 9.4.4 Results.- 9.5 Conclusion.- References.- 10. AI Modelling for Data Quality Control Xiaohui Liu.- 10.1 Introduction.- 10.2 Statistical Approaches to Outliers.- 10.3 Outlier Detection and Analysis.- 10.4 Visual Field Test.- 10.5 Outlier Detection.- 10.5.1 Self-Organising Maps (SOM).- 10.5.2 Applications of SOM.- 10.6 Outlier Analysis by Modelling 'Real Measurements'.- 10.7 Outlier Analysis by Modelling Noisy Data.- 10.7.1 Noise Model I: Noise Definition.- 10.7.2 Noise Model II: Construction.- 10.7.3 Noise Elimination.- 10.8 Concluding Remarks.- References.- 11. New Directions in Text Categorization.- 11.1 Introduction.- 11.2 Machine Learning for Text Classification.- 11.3 Radial Basis Functions and the Bard.- 11.4 An Evolutionary Algorithm for Text Classification.- 11.5 Text Classification by Vocabulary Richness.- 11.6 Text Classification with Frequent Function Words.- 11.7 Do Authors Have Semantic Signatures?.- 11.8 Syntax with Style.- 11.9 Intermezzo.- 11.10 Some Methods of Textual Feature-Finding.- 11.10.1 Progressive Pairwise Chunking.- 11.10.2 Monte Carlo Feature Finding.- 11.10.3 How Long Is a Piece of Substring?.- 11.10.4 Comparative Testing.- 11.11 Which Methods Work Best? - A Benchmarking Study.- 11.12 Discussion.- 11.12.1 In Praise of Semi-Crude Bayesianism.- 11.12.2 What's So Special About Linguistic Data?.- References.
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