Utility-based learning from data /
""Utility-Based Learning from Data is an excellent treatment of data-driven statistics for decision-making. Friedman and Sandow lucidly describe the connections between different branches of statistics and econometrics, such as utility theory, maximum entropy, and Bayesian analysis. A must...
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| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press,
c2011
Boca Raton, FL : ©2011 |
| Series: | Chapman & Hall/CRC machine learning & pattern recognition series
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| Subjects: |
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| 100 | 1 | |a FRIEDMAN, CRAIG |1 http://viaf.org/viaf/122101670 | |
| 100 | 1 | |a Friedman, Craig | |
| 245 | 1 | 0 | |a Utility-based learning from data / |c Craig Friedman, Sven Sandow |
| 260 | |a Boca Raton, FL : |b CRC Press, |c c2011 | ||
| 260 | |a Boca Raton, FL : |b CRC Press, |c ©2011 | ||
| 300 | |a 1 online resource (xix, 397 pages) : |b illustrations | ||
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| 490 | 1 | |a Chapman & Hall/CRC machine learning & pattern recognition series | |
| 500 | |a Description based on print version record | ||
| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | |a Introduction; Notions from Utility Theory; Model Performance Measurement; Model Estimation; The Viewpoint of This Book; Organization of This Book; Examples Mathematical Preliminaries ; Some Probabilistic Concepts; Convex Optimization; Entropy and Relative Entropy The Horse Race; The Basic Idea of an Investor in a Horse Race; The Expected Wealth Growth Rate; The Kelly Investor; Entropy and Wealth Growth Rate; The Conditional Horse Race Elements of Utility Theory ; Beginnings: The St | |
| 505 | 0 | |a Measures and MRE for Leveraged Investors; Model Performance Measures and MRE for Investors in Incomplete Markets; Utility-Based Performance Measures for Regression Models Select Applications; Three Credit Risk Models; The Gail Breast Cancer Model; A Text Classification Model References Index Exercises appear at the end of most chapters | |
| 505 | 0 | |a Model Performance Measurement ; Utility-Based Performance Measures for Discrete Probability Models; Revisiting the Likelihood Ratio Utility-Based Performance Measures for Discrete Conditional Probability Models; Utility-Based Performance Measures for Probability Density Models; Utility-Based Performance Measures for Conditional Probability Density Models; Monetary Value of a Model Upgrade; Some Proofs Select Methods for Estimating Probabilistic Models ; Classical Parametric Methods; Regularized Maximum Likelihood Inference; Bayesian Inference; Minimum Relative Entropy (MRE) Methods A Utility-Based Approach to Probability Estimation ; Discrete Probability Models; Conditional Density Models; Probability Estimation via Relative U -Entropy Minimization; Expressing the Data Constraints in Purely Economic Terms; Some Proofs Extensions; Model Performance | |
| 505 | 0 | |a Petersburg Paradox; Axiomatic Approach; Risk Aversion; Some Popular Utility Functions; Field Studies; Our Assumptions The Horse Race and Utility; The Discrete Unconditional Horse Races; Discrete Conditional Horse Races; Continuous Unconditional Horse Races; Continuous Conditional Horse Races Select Methods for Measuring Model Performance; Rank-Based Methods for Two-State Models; Likelihood; Performance Measurement via Loss Function A Utility-Based Approach to Information Theory ; Interpreting Entropy and Relative Entropy in the Discrete Horse Race Context; (U, O )-Entropy and Relative (U, O )-Entropy for Discrete Unconditional Probabilities; Conditional (U, O )-Entropy and Conditional Relative (U, O )-Entropy for Discrete Probabilities; U -Entropy for Discrete Unconditional Probabilities Utility-Based | |
| 520 | 1 | |a ""Utility-Based Learning from Data is an excellent treatment of data-driven statistics for decision-making. Friedman and Sandow lucidly describe the connections between different branches of statistics and econometrics, such as utility theory, maximum entropy, and Bayesian analysis. A must-read for serious statisticians! Utility-Based Learning from Data connects key ideas from utility theory with methods from statistics, machine learning, and information theory. It presents, using decision-theoretic principles, a framework for building models that can be used by decision makers. By adopting the utility-based approach, Friedman and Sandow are able to adapt models to the risk preferences of the model user, while maintaining tractability. It is a much-needed and comprehesive book, which should help put model-building for use by decision makers on more solid ground."--Gregory Piatetsky-Shapiro, editor of KDnuggets.com, co-founder and past chair of SIGKDD, and founder of the Knowledge Discovery and Data Mining (KDD) conferences" "This book provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used to make decisions. By taking this point of view, the book sheds light on and generalizes some popular statistical learning approaches, connecting ideas from information theory, statistics, and finance. It strikes a balance between rigor and intuition, conveying the main ideas to as wide an possible."--Jacket | |
| 538 | |a Mode of access: World Wide Web | ||
| 588 | 0 | |a Print version record | |
| 650 | 0 | |a Machine learning | |
| 650 | 6 | |a Apprentissage automatique | |
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| 700 | 1 | |a Sandow, Sven |1 http://viaf.org/viaf/76997150 | |
| 700 | 1 | |a Sandow, Sven | |
| 776 | 0 | 8 | |i Print version: |a Friedman, Craig |t Utility-based learning from data. |d Boca Raton, FL : CRC Press, ©2011 |z 9781584886228 |w (DLC) 2010023847 |w (OCoLC)144565539 |
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