Generalized robust conjoint estimation
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Giorgos Zacharia | Theodoros Evgeniou | Constantinos Boussios | T. Evgeniou | G. Zacharia | C. Boussios
[1] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[2] D. McFadden. Conditional logit analysis of qualitative choice behavior , 1972 .
[3] Allan D. Shocker,et al. Linear programming techniques for multidimensional analysis of preferences , 1973 .
[4] A. Tversky,et al. Judgment under Uncertainty: Heuristics and Biases , 1974, Science.
[5] C. Manski. The structure of random utility models , 1977 .
[6] A. N. Tikhonov,et al. Solutions of ill-posed problems , 1977 .
[7] P. Green,et al. Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .
[8] Paul E. Green,et al. Robustness of Conjoint Analysis: Some Monté Carlo Results , 1978 .
[9] Madhav N. Segal,et al. Reliability of Conjoint Analysis: Contrasting Data Collection Procedures , 1982 .
[10] Naresh K. Malhotra,et al. Improving Predictive Power of Conjoint Analysis by Constrained Parameter Estimation , 1983 .
[11] C. J. Stone,et al. Additive Regression and Other Nonparametric Models , 1985 .
[12] D. McFadden. The Choice Theory Approach to Market Research , 1986 .
[13] Mark D. Uncles,et al. Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .
[14] Philippe Cattin,et al. Commercial Use of Conjoint Analysis: An Update , 1989 .
[15] Paul E. Green,et al. Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .
[16] Grace Wahba,et al. Spline Models for Observational Data , 1990 .
[17] M. Ben-Akiva,et al. A Multinational Probit Formulation for Large Choice Sets , 1991 .
[18] N. Alon,et al. Scale-sensitive Dimensions, Uniform Convergence, , 1993 .
[19] Noga Alon,et al. Scale-sensitive dimensions, uniform convergence, and learnability , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.
[20] Mark J. Garratt,et al. Efficient Experimental Design with Marketing Research Applications , 1994 .
[21] Jordan J. Louviere,et al. Modeling Hierarchical Conjoint Processes with Integrated Choice Experiments , 1994 .
[22] Robert E. Schapire,et al. Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.
[23] Karl T. Ulrich,et al. Product Design and Development , 1995 .
[24] M. Ben-Akiva,et al. Discrete choice models with latent choice sets , 1995 .
[25] J. Carroll,et al. Guest Editorial: Psychometric Methods in Marketing Research: Part I, Conjoint Analysis , 1995 .
[26] J. Douglas Carroll,et al. Psychometric Methods in Marketing Research: Part I, Conjoint Analysis , 1995 .
[27] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[28] Tomaso A. Poggio,et al. Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.
[29] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[30] Bernhard Schölkopf,et al. Incorporating Invariances in Support Vector Learning Machines , 1996, ICANN.
[31] P. Lenk,et al. Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs , 1996 .
[32] Michel Wedel,et al. Representing heterogeneity in consumer response models , 1997 .
[33] Pradeep K. Chintagunta,et al. Representing Heterogeneity in Consumer Response Models 1996 Choice Conference Participants , 1997 .
[34] Jaideep Srivastava,et al. Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.
[35] M. Ben-Akiva,et al. Modeling Methods for Discrete Choice Analysis , 1997 .
[36] John N. Tsitsiklis,et al. Introduction to linear optimization , 1997, Athena scientific optimization and computation series.
[37] Noga Alon,et al. Scale-sensitive dimensions, uniform convergence, and learnability , 1997, JACM.
[38] Massimiliano Pontil,et al. Properties of Support Vector Machines , 1998, Neural Computation.
[39] Trevor Hastie,et al. Additive Logistic Regression : a Statistical , 1998 .
[40] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[41] Greg M. Allenby,et al. On the Heterogeneity of Demand , 1998 .
[42] Michel Bierlaire,et al. DISCRETE CHOICE MODELS , 1998 .
[43] Peter E. Rossi,et al. Marketing models of consumer heterogeneity , 1998 .
[44] Greg M. Allenby,et al. A Hierarchical Bayes Model of Primary and Secondary Demand , 1998 .
[45] Tomaso Poggio,et al. Incorporating prior information in machine learning by creating virtual examples , 1998, Proc. IEEE.
[46] Daphne Koller,et al. Support Vector Machine Active Learning with Application sto Text Classification , 2000, ICML.
[47] D. Hensher,et al. Stated Choice Methods: Analysis and Applications , 2000 .
[48] Kristin P. Bennett,et al. Duality and Geometry in SVM Classifiers , 2000, ICML.
[49] Ralf Herbrich,et al. Large margin rank boundaries for ordinal regression , 2000 .
[50] Thore Graepel,et al. Large Margin Rank Boundaries for Ordinal Regression , 2000 .
[51] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[52] Gunnar Rätsch,et al. Active Learning in the Drug Discovery Process , 2001, NIPS.
[53] Ji Zhu,et al. Kernel Logistic Regression and the Import Vector Machine , 2001, NIPS.
[54] Joel Huber,et al. Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments , 2001 .
[55] John R. Hauser,et al. Fast Polyhedral Adaptive Conjoint Estimation , 2002 .
[56] Daphne Koller,et al. Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..
[57] Ron Kohavi,et al. Mining e-commerce data: the good, the bad, and the ugly , 2001, KDD '01.
[58] Kristin P. Bennett,et al. MARK: a boosting algorithm for heterogeneous kernel models , 2002, KDD.
[59] Rick L. Andrews,et al. Hierarchical Bayes versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery , 2002 .
[60] Tomaso Poggio,et al. Everything old is new again: a fresh look at historical approaches in machine learning , 2002 .
[61] K. Pauwels. How Dynamic Consumer Response, Dynamic Competitor Response and Expanded Company Action Shape Longterm Marketing Effectiveness , 2003 .
[62] Kamel Jedidi,et al. Measuring Heterogeneous Reservation Prices for Product Bundles , 2003 .
[63] John R. Hauser,et al. Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis , 2004 .
[64] Tomaso A. Poggio,et al. Image Representations and Feature Selection for Multimedia Database Search , 2003, IEEE Trans. Knowl. Data Eng..
[65] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[66] K. Pauwels. How Dynamic Consumer Response, Competitor Response, Company Support, and Company Inertia Shape Long-Term Marketing Effectiveness , 2004 .
[67] Tomaso A. Poggio,et al. Statistical Learning Theory: A Primer , 2000, International Journal of Computer Vision.
[68] David J. Curry,et al. Prediction in Marketing Using the Support Vector Machine , 2005 .