Other References: |
Haberman et al., “Generalised linear models in actuarial work”, Presentation Actuaarial Society Feb. 2, 1998.
Schlesinger, Insurance Demand Without the Expected-Utility Paradigm, Journal of Risk and Insurance, Oct. 1996.
Nakao et al., Computing 1989 Occupational Prestige Scores, General Social Survey Methodological Report, 1990.
Collins et al., Mortgage Underwriting Judgements, IEEE Service Center, 1988.
Flores et al., Robust Logistic Regression for Insurance, University Carlos III of Madrid, Nov. 2001.
Borglin et al., Stohcastic Dominance and Conditional Expectation, The Geneva Papers on Risk and Insurance Theory, 27:31-48,2002.
Keeney et al., Decisions with Multiple Objective: Preferences and Value Tradeoffs book, 1976, John Wiley & Sons, Inc., preface pages, acknowledgement page, Chapter Headings p. xv, Contents pp. xvii-xxviii.
Duda et al., “Pattern Classification” book, 2nd Edition, John Wiley & Songs, Inc., 2001, Contents pp. vii-xvi, Preface pp. xvii-xx.
Shafer, “A Mathematical Theory of Evidence” book, published by Princeton University Press, Princeton and London, 1976, Preface pp. ix-x, Contents pp. xi-xiii.
Kuncheva et al., “Designing Classifier Fusion System by Genetic Algorithms,” IEEE Transactions of Evolutionary Computation, vol. 4, No. 4, Sep. 2000, pp. 327-336.
Chibelushi et al., “Adaptive Classifier Integration for Robust Pattern Recognition,” IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 29, No. 6, Dec. 1999, pp. 902-907.
Dempster, “Upper and Lower Probabilities Induced By A Multivalued Mapping,” Annals of Mathematical Statistics, Harvard University, 38:, pp. 325-339, 1967.
Bonissone et al., “Selecting Uncertainty Calculi and Granularity and Experiment in Trading-Off Precision and Complexity,” Uncertainty in Artificial Intelligence, Kanal & Lemmer (editors), pp. 217-247, Elsevier Science Publishers B.V. (North-Holland), 1986.
Bonissone, “Summarizing and Propagating Uncertain Information With Triangular Norms,” International Journal of Approximate Reasoning, Elsevier Science Publishing Co., Inc., New York, NY, 1: pp. 71-101, 1987.
Krzyzak et al., “Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition,”IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, No. 3, pp. 418-435, May/Jun. 1992.
Fairhurst et al., “Enhancing Consensus in Multiple Expert Decision Fusion,” IEE Proceedings Vis. Image Signal Process, vol. 147, No. 1, 2000.
Ruspini, “Epistemic Logics, Probability, and the Calculus of Evidence,” Proc. Tenth Intern Joint Conf. on Artificial Intelligence, Milan, Italy, Reasoning pp. 924-931, 1987.
Schweizer et al., “Associative Functions and Abstract Semigroups,” Publicationes Mathematicae Debrecen, 10:pp. 69-81, 1963.
Srivastava et al., “A Hybrid Neural Network Model for Fast Voltage Contingency Screening and Ranking,” International Journal of Electrical Power & Energy Systems, vol. 22, No. 1, pp. 35-42.
Lampinen et al., “Selection of Training Samples for Learning With Hints,” IJCNN 1999 IEEE, International Joint Conference on Neural Networks, vol. 2, pp. 1438-1441.
Abu-Mostafa, “Hints and the VC Dimension,” Neural Computation, vol. 5, No. 2, Mar. 1993, pp. 278-288.
Ding et al., “Multi-class Protein Fold Recognition Using Support Vector Machines and Neural Networks,” Bioinformatics, vol. 17, No. 4, 2001, pp. 349-358.
Dorizzi et al., “Cooperation and Modularity for Classification Through Neural Network Techniques,” Proceedings of 1993 International Conference on System, Man and cybernetics, vol. 3, pp. 469-474.
Price et al., “Pairwise Neural Network Classifiers with Probabilistic Outputs,” Tesauro et al., eds., Neural Information Processing Systems, vol. 7, 1994, pp. 1109-1116.
Vaughn et al., “Interpretation and Knowledge Discovery from a Multilayer Perception Network that Performs Whole Life Assurance Risk Assessment,” Neural Computing & Applications, Springer-Verlag London Limited, vol. 6, 1997, pp. 201-213.
Collins et al., “An Application of a Multiple Neural Network Learning System to Emulation of Mortgage Underwriting Judgements,” Proceedings of the IEEE International Conference on Neural Networks, 1988, pp. II-459 through II-466.
Nossek et al., “Classification Systems Based on Neural Networks,” the Fifth IEEE International Workshop on Cellular Neural Networks and their Applications, London, England, Apr. 14-17, 1998, pp. 26-33.
Abu-Mostafa, “A Method for Learning from Hints,” in Hanson, Cowan & Giles eds., Advances in Neural Information Processing Systems, vol. 5, pp. 73-80, Morgan Kaufmann, San Mateo, CA.
Beradi et al., “The Effect of Misclassification Costs on Neural Network Classifiers,” Decision Sciences, vol. 30, No. 3, summer 1999, pp. 659-682.
Tong et al., “Linguistic Approach to Decisionmaking with Fuzzy Sets,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-10, No. 11, Nov. 1990, pp. 716-723.
MacCrimmon, “An Overview of Multiple Objective Decision Making,” editors Cochrane and Zeleny, 1973, pp. 18-44.
Kung et al., “On Finding the Maxima of a Set of Vectors,” Journal of the Association for Computing Machinery, vol. 22, No. 4, Oct. 1975, pp. 469-476.
Bentley et al., “On the Average Number of Maxima in a Set of Vectors and Applications,” Journal of the Association for Computing Machinery, vol. 25, No. 4, Oct. 1978, pp. 536-543.
Simon, “A Behavioral Model of Rational Choice,” Quarterly Journal of Economics, vol. 69, No. 1, Feb. 1955, pp. 99-118.
Zionts, “Decision Making: Some Experiences, Myths and Observations,” Multiple criteria Decision Making: Proceedings of the Twelfth International Conference, Lecture notes in Economics and Mathematical Systems, vol. 448, Hagen (Germany), 1997, pp. 233-241.
Friedman, “Multivariate Adaptive Regression Splines,” Annals of Statistics, SLAC PUB-4960 Rev, Tech Report 102 Rev, Aug. 1990, pp. 1-79.
Schumaker, Discussion of Friedman paper, “Multivariate Adaptive Regression Splines,” pp. 1-2.
Owen, Discussion of Friedman paper, “Multivariate Adaptive Regression Splines,” pp. 1-9 & Figs. 1-4.
Stone, Discussion of Friedman paper, “Multivariate Adaptive Regression Splines,” pp. 1-3.
Sullivan, Some Comments on Friedman paper, “Multivariate Adaptive Regression Splines,” pp. 1-6.
Breiman, Discussion of Friedman paper, “Multivariate Adaptive Regression Splines,” 8 pages.
Golubev et al., Discussion of Friedman paper, “Multivariate Adaptive Regression Splines,” 2 pages.
Buja et al., Discussion of Friedman paper, “Multivariate Adaptive Regression Splines,” pp. 1-5.
Gu et al., Comments on Friedman paper, “Multivariate Adaptive Regression Splines,” pp. 1-10, rev. Jun. 5, 1990.
Barron et al., Discussion of Friedman paper, “Multivariate Adaptive Regression Splines” pp. 1-12 and 2 pages of Tables, Jul. 1990, University of IL.
Friedman, “Rejoinder,” pp. 1-14.
Gonzalez et al., “A case-based reasoning approach to real estate property appraisal,” Systems with Applications, vol. 4, pp. 229-246. 1992.
Bonissone et al., “Evolutionary optimization of fuzzy decision systems for automated insurance underwriting,” Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, May 12-17, 2002, pp. 1003-1008.
Bonissone et al, “Fuzzy case-based resoning for decision making,” 2001 IEEE International Fuzzy Systems Conference, pp. 995-998.
Goetz, “A Fuzzy Future For MIS?”, Informationweek, p. 51, Feb. 25, 1991, ISSN: 8750-6874. Dialog ID No. 01544473, From Dialog file 16 (Gale Group).
Allwein et al., “Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers”, Journal of Machine Learning Research, vol. 1, (2000), pp. 113-141.
Rubtsov et al., “A Unified Format for Trained Neural Network Description”, Neural Network, Proceedings IJCNN '01 International Joint Conference, vol. 4, pp. 2367-2372, Meeting date: Jul. 15-19, 2001.
Yang, “An Evaluation of Statistical Approaches to Text Categorization”, Kluwer Academic Publishers, Information Retrieval 1, pp. 69-90.
Foltin et al., “Beyond expert systems: Neural networks in accounting,” National Public Accountant, Jun. 1996, pp. 26-30, v41n6.
Humpert, Bidirectional Associative Memory with Several Patterns, 1990, ISU, CS-122, I-741-I-750.
Waxman et al., “Information Fusion for Image Analysis: Geospatial Foundations for Higher Fusion”, 2002, ISIF, 562-569.
Yap, Jr. et al., “Generalized Associative Memory Models for Data Fusion”, 2003, IEEE, 0-7803-7898-Sep. 2003, 2528-2533.
Goetz, “Clearing up fuzzy logic—Forum—Column”, Software Magazine, Jan. 1992, Wiesner Publications, Inc.
Watje et al., U.S. Appl. No. 09/510,535.
Apte et al., Ramp: Rules Abstraction for Modeling and Prediction, IBM Research Division Technical Report RC-20271, IBM T.J. Watson Research Center, Jan. 12, 1996, pp. 1-14.
Finger, Robert J., Chapter 6 Risk Classification, Foundations of Casualty Actuarial Science, 4th Edition 2001, pp. 287-341.
Murphy et al., Using Generalized Linear Models to Build Dynamic Pricing Systems, Casualty Actuarial Society, Winter 2000, pp. 106-139.
Brockett et al., Operations Research In Insurance: A Review, Transactions of Society of Actuaries, 1995, vol. 47, pp. 7-87.
Aggour et al., Automating the Underwriting of Insurance Applications, AI Magazine, Fall 2006, pp. 36-50.
Haberman, et al., Generalised Linear Models in Actuarial Work, Presented to the Staple Inn Actuarial Society, Feb. 2, 1998.
Borglin, et al., Stochastic Dominance and Conditional Expectation—An Theoretical Approach, The Geneva Papers on risk and Insurance Theory, 27:31-48, 2002.

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