Prediction in Marketing Using the Support Vector Machine

References

• Abraham Magid, Lodish Len. Promoter: An automated promotion evaluation system. Marketing Sci. (1987) 6(1):1–25Link, Google Scholar
• Abraham Magid, Lodish Len. An implemented system for improving promotion productivity using store scanner data. Marketing Sci. (1993) 12(3):248–269Link, Google Scholar
• Allenby Greg M., Arora Neeraj, Ginter James L. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 35(May):152–162Crossref, Google Scholar
• Allenby Greg, Arora Neeraj, Diener Chris, Kim Jaehwan, Lotti Mike, Markowitz Paul. Distinguishing likelihoods, loss functions and heterogeneity in the evaluation of marketing models. Canadian J. Marketing Res. (2002) 20(1):44–59Google Scholar
• Andrews Rick L., Ansari Asim, Currim Imran S. Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery. J. Marketing Res. (2002) 39(February):87–98Crossref, Google Scholar
• Ariely Dan, Lynch, Aparicio IV John G. Learning by collaborative and individual-based recommendation agents. J. Consumer Psych. (2004) 14(1 & 2):81–95Crossref, Google Scholar
• Avery Christopher, Resnick Paul, Zeckhauser Richard. The market for evaluations. Amer. Econom. Rev. (1999) 89(June):564–584Crossref, Google Scholar
• Bellman Richard E.Adaptive Control Processes: A Guided Tour (1961) (Princeton University Press, Princeton, NJ) Crossref, Google Scholar
• Ben-Akiva Moshe, Lerman Steven R.Discrete Choice Analysis: Theory and Application to Travel Demand (1985) (MIT Press, Cambridge, MA) Google Scholar
• Ben-Akiva Moshe, McFadden Daniel, Makoto Abe, Bockenholt Ulf, Bolduc Denis, Gopinath Dinesh, Morikawa Takayuki, Ramaswamy Venkatram, Rao Vithala, Revelt David, Steinberg Dan. Modeling methods for discrete choice analysis. Marketing Lett. (1997) 8(3):273–286Crossref, Google Scholar
• Berry Michael, Linoff Gordon. Data Mining Techniques: for Marketing, Sales, and Customer Support (1997) (John Wiley and Sons, New York) Google Scholar
• Blattberg Robert C., Kim Byung-Do, Ye Jianming, Blattberg Robert C., Glazer Rashi, Little Manuel. Large-scale databases: The new marketing challenge. The Marketing Information Revolution (1994) (Harvard Business School Press, Boston, MA) 173–203Google Scholar
• Boser Bernhard E., Guyon Isabelle M., Vapnik Vladimir. A trained algorithm for optimal margin classifier. Fifth Annual Workshop on Computational Learning Theory (1992) (ACM, Pittsburgh, PA) 144–151Crossref, Google Scholar
• Bradlow Eric T. Current issues and a “wish list” for conjoint analysis. (2003) 1–10Working paper, The Wharton School of the University of Pennsylvania, Philadelphia, PAGoogle Scholar
• Brazerman Max H.Judgment in Managerial Decision Making (1994) 3rd ed.(J. Wiley, New York) Google Scholar
• Bronson Richard. Matrix Methods: An Introduction (1991) 2nd ed.(Academic Press, Inc., Boston, MA) Google Scholar
• Bucklin Randolph E., Lehmann Donald R., Little John D. C. From decision support to decision automation: A 2020 vision. Marketing Lett. (1998) 9(3):235–246Crossref, Google Scholar
• Bucklin Randolph E., Lattin James M., Ansari Asim, Gupta Sunil, Bell David, Coupey Eloise, Little John D. C., Mela Carl, Montgomery Alan, Steckel Joel. Choice and the Internet: From clickstream to research stream. Marketing Lett. (2002) 13(3):245–258Crossref, Google Scholar
• Burges Christopher J. C. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery (1998) 2:121–167Crossref, Google Scholar
• Chapelle Olivier, Hallner Patrick, Vapnik Vladimir. SVM for histogram-based image classification. IEEE Trans. Neural Networks (1999) 10(5):1055–1064Crossref, Google Scholar
• Cooper Lee G., Giuffrida Giovanni. Turning datamining into a management science tool. Management Sci. (2000) 46(2):249–264Link, Google Scholar
• Cortes Corinna, Vapnik Vladimir. Support vector networks. Machine Learning (1995) 20:273–297Crossref, Google Scholar
• Courant Rourant, Hilbert David. Methods of Mathematical Physics (1953) 1(Interscience Publishers, Inc., New York) Google Scholar
• Cristianini Nello, Shawe-Taylor John. An Introduction to Support Vector Machines—and Other Kernel-Based Learning Methods (2000) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
• Cui Dapeng, Curry David. Applications of support vector machines in marketing: An exposition. (2003) . Working paperGoogle Scholar
• Dawes Robyn M., Corrigan B. Linear models in decision-making. Psych. Bull. (1974) 81:95–106Crossref, Google Scholar
• Diehl Kristin R., Kornish Laura J., Lynch John G. Smart agents: When lower search costs for quality information increase price sensitivity. J. Consumer Res. (2003) 30(June):56–71Crossref, Google Scholar
• Domencich Thomas A., McFadden Daniel. Urban Travel Demand: A Behavioral Analysis (1975) (North-Holland Publishing Co., Amsterdam, The Netherlands) Google Scholar
• Edgington Eugene S.Randomization Tests (1995) 3rd ed.(Marcel Dekker, Inc., New York) Google Scholar
• Efron Bradley, Tibshirani Robert J.An Introduction to the Bootstrap (1993) (Chapman and Hall, New York) Crossref, Google Scholar
• Einhorn Hillel J. The use of nonlinear, non-compensatory models in decision making. Psych. Bull. (1970) 73:221–230Crossref, Google Scholar
• Evgeniou Theodoros, Boussios Constantinos, Zacharia Giorgos. Generalized robust conjoint estimation. Marketing Sci. (2005) 24(3):415–429Link, Google Scholar
• Fisher Ronald A.Contributions to Mathematical Statistics (1950) (Wiley, New York) Google Scholar
• Friedman Jerome H. On bias, variance, 0/1—Loss, and the curse of dimensionality. Data Mining Knowledge Discovery (1997) 1:55–77Crossref, Google Scholar
• Gershoff Andrew, West Patricia M. Using a community of knowledge to build intelligent agents. Marketing Lett. (1998) 9(January):79–91Crossref, Google Scholar
• Gilbride Timothy J., Allenby Greg M. A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Sci. (2004) 23(3):391–406Link, Google Scholar
• Good Phillip I.Resampling Methods: A Practical Guide to Data Analysis (2005) XX3rd ed.(Birkhäuser Book (Springer), Boston, MA) Google Scholar
• Guadagni Peter M., Little John D. C. A logit model of brand choice calibrated on scanner data. Marketing Sci. (1983) 2(3):203–238Link, Google Scholar
• Guyon Isabelle, Elisseeff André. An introduction to variable and feature selection. J. Machine Learning Res. (2003) 3(March):1157–1182Google Scholar
• Hahn G. J., Shapiro S. S. A catalog and computer program for the design and analysis of orthogonal symmetric and asymmetric fractional factorial experiments. (1966) . Technical report no. 66-C-165, General Electric Research and Development Center, Schenectady, NYGoogle Scholar
• Häubl Gerald, Trifts Valerie. Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Sci. (2000) 19(1):4–21Link, Google Scholar
• Hempel Carl G. Aspects of scientific explanation. Aspects of Scientific Explanation and Other Essays in the Philosophy of Science (1965) (The Free Press, New York) 331–496Google Scholar
• Hinton G. E., McClelland J. L., Rumelhart D. E., Rumelhart D. E., McClelland J. L.the PDP Research Group. the PDP Research Group Distributed representations. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (1986) (MIT Press, Cambridge, MA) 77–109Google Scholar
• Hofstede Frenkel Ter, Kim Youngchan, Wedel Michel. Bayesian prediction in hybrid conjoint analysis. J. Marketing Res. (2002) 39(May):253–261Crossref, Google Scholar
• Hunt Shelby D.Marketing Theory: The Philosophy of Marketing Science (1983) (Richard D. Irwin, Inc., Homewoods, IL) Google Scholar
• Iacobucci Dawn, Arabie Phipps, Bodapati Anand. Recommendation agents on the Internet. J. Interactive Marketing (2000) 14(3):2–11Crossref, Google Scholar
• Kahneman Daniel. Maps of bounded rationality: A perspective on intuitive judgment and choice. Nobel Prize Lecture (2002) December 8):449–489 http://nobelprize.org/ economics/laureates/2002Google Scholar
• Kamakura Wagner A., Wedel Michel, de Rosa Fernando, Mazzon Jose Afonso. Cross-selling through database marketing: A mixed data factor analyzer for data augmentation and prediction. Internat. J. Res. Marketing (2003) 20(1):45–65Crossref, Google Scholar
• Kardes Frank R.Consumer Behavior and Managerial Decision Making (1999) (Addison-Wesley, Reading, MA) Google Scholar
• Krebel Ulrich, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Pairwise classification and support vector machines. Advances in Kernel Methods—Support Vector Learning (1999) (MIT Press, Cambridge, MA) 255–268Google Scholar
• Langley Keith, Burgess Neil. Linear and nonlinear hidden units in artificial neural networks. (2000) . Under reviewGoogle Scholar
• Lilien Gary L., Rangaswamy Arvind, van Bruggen Gerrit H., Wierenga Berend. Bridging the marketing theory-practice gap with marketing engineering. J. Bus. Res. (2002) 55:111–121Crossref, Google Scholar
• Little John D. C. Marketing automation on the Internet. 5th Invitational Choice Symposium (2001) . U. C. Berkeley/Asilomar, June 1–5, Berkeley, CAGoogle Scholar
• Luce Duncan R.Individual Choice Behavior: A Theoretical Analysis (1959) (Wiley, New York) Google Scholar
• MacKay David J. C., Oldfield Martin J. Generalization error and the number of hidden units in a multilayer perceptron. (1995) . Working paper, Cambridge University, Cambridge, UKGoogle Scholar
• Mattern David, Haykin Simon, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Support vector machines for dynamic reconstruction of a chaotic system. Advances in Kernel Methods—Support Vector Learning (1999) (MIT Press, Cambridge, MA) 211–242Google Scholar
• Minsky Marvin, Papert Seymour. Perceptrons: An Introduction to Computational Geometry (1969) (MIT Press, Cambridge, MA) Google Scholar
• Moe Wendy W., Fader Peter S. Dynamic conversion behavior at e-commerce sites. Management Sci. (2004) 50(3):326–335Link, Google Scholar
• Müller Klaus-Robert, Smola Alexander J., Rätsch Gunnar, Schökopf Bernhard, Kohlmorgen Jens, Vapnik Vladimir, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Using support vector machines for time series prediction. Advances in Kernel Methods—Support Vector Learning (1999) (MIT Press, Cambridge, MA) 243–253Google Scholar
• Osuna Edgar, Freund Robert, Girosi Federico. Training support vector machines: An application to face detection. Proc. Comput. Vision Pattern Recognition (1997) 130–136Crossref, Google Scholar
• Platt John C., Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods—Support Vector Learning (1999) (MIT Press, Cambridge, MA) 185–208Google Scholar
• Platt John C., Smola Alexander J., Bartlett Peter L., Schölkopf Bernhard, Schuurmans Dale. Probabilities for SV machines. Advances in Large Margin Classifiers (2000) (MIT Press, Cambridge, MA) 61–74Google Scholar
• Platt John C., Cristianni Nello, Shawe-Taylor John, Solla S. A., Leen T. K., Müller K.-R. Large margin DAGs for multiclass classification. Advances in Neural Information Processing Systems (2000) (MIT Press, Cambridge, MA) 547–553Google Scholar
• Politz Alfred, Deming W. Edwards. On the necessity to present consumer preferences as predictions. J. Marketing (1953) July). [Reprinted in Marketing Res. (June 1990) 50–55.]Google Scholar
• Ratner Bruce. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data (2003) (CRC Press). http://dmstat1.comCrossref, Google Scholar
• Rossi Peter E., Allenby Greg M. Bayesian statistics and marketing. Marketing Sci. (2003) 22:304–328Link, Google Scholar
• Russo J. Edward, Shoemaker Paul J. H.Decision Traps (1989) (Doubleday/Currency, New York) Google Scholar
• Sándor Zsolt, Kenneth, Train. Quasi-random simulation of discrete choice models. Transportation Res. Part B (2004) 38:313–327Crossref, Google Scholar
• Schmitz John, Armstrong Gordon O., Little John D. C. CoverStory—Automated news finding in marketing. Interfaces (1990) 20(6):29–38Link, Google Scholar
• Schölkopf Bernhard, Smola Alex J., Müller Klaus-Robert, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Kernel principle component analysis. Advances in Kernel Methods—Support Vector Learning, Advances in Kernel Methods (1999) (MIT Press, Cambridge, MA) 327–352Google Scholar
• Schölkopf Bernhard, Barlett Peter, Smola Alex, Williamson Robert, Kearns Michael S., Solla Sara A., Cohn David A. Shrinking the tube: A new support vector regression algorithm. Advances in Neural Information Processing Systems (1999) (MIT Press, Cambridge, MA) 330–336Google Scholar
• Schölkopf Bernhard, Simard P., Smola Alex J., Vapnik Vladimir, Jordan Michael I., Kearns Michael S., Solla Sara A. Prior knowledge in support vector kernels. Advances in Neural Information Processing Systems (1998) 10(MIT Press, Cambridge, MA) 640–646Google Scholar
• Shawe-Taylor John, Cristianini Nello, Smola A. J., Bartlett P., Scholkopf B., Schuurmans C. Margin distribution and soft margin. Advances in Large Margin Classifiers (1999a) (MIT Press, Cambridge, MA) 349–357Google Scholar
• Shawe-Taylor John, Cristianini Nello. Margin distribution bounds on generalization. Proc. Eur. Conf. Comput. Learning Theory, EuroColt’99 (1999b) 263–273Crossref, Google Scholar
• Shawe-Taylor John, Cristianini Nello. Further results on the margin distribution. Proc. Conf. Comput. Learning Theory (1999c) 278–285COLT 99Crossref, Google Scholar
• Sismeiro Catarina, Bucklin Randolph E. Modeling purchase behavior at an e-commerce web site: A task completion approach. J. Marketing Res. (2004) XLI:306–323Crossref, Google Scholar
• Smith Michael D., Brynjolfsson Erik. Consumer decision making at an Internet Shopbots brand still matters. J. Indust. Econom. (2001) XLIX(4):541–558Crossref, Google Scholar
• Stitson Mark O., Gammerman Alex, Vapnik Vladimir, Vovk Volodya, Watkins Chris, Weston Jason, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Support vector regression with ANOVA decomposition kernels. Advances in Kernel Methods—Support Vector Learning (1999) (MIT Press, Cambridge, MA) 285–292Google Scholar
• Thurstone L. L. A law of comparative judgment. Psych. Rev. (1927) 34:273–286Crossref, Google Scholar
• Toubia Oliver, Hauser John R., Simester Duncan I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41(February):116–131Crossref, Google Scholar
• Tversky Amos. Elimination by aspects: A theory of choice. Psych. Rev. (1972) 79(July):281–299Crossref, Google Scholar
• Vapnik Vladimir. Estimation of Dependences Based on Empirical Data (1979) (Springer Verlag, New York) Google Scholar
• Vapnik Vladimir. The Nature of Statistical Learning Theory (1995) (Springer Verlag, New York) Crossref, Google Scholar
• Vapnik Vladimir. Statistical Learning Theory (1998) (Wiley, New York) Google Scholar
• Vapnik Vladimir, Chervonenkis A. A note on one class of perceptrons. Automation Remote Control (1964) 25(1Google Scholar
• Vapnik Vladimir, Chervonenkis A. On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Its Appl. (1971) 16(2):264–280Crossref, Google Scholar
• Vapnik Vladimir, Chapelle Olivier, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Bounds on error expectation for SVM. Advances in Large Margin Classifiers (2000) (MIT Press, Cambridge, MA) 261–280Google Scholar
• Viaene Stijn, Derrig Richard A., Baesens Bart, Dedene Guido. A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. J. Risk Insurance (2002) 69(3):373–421Crossref, Google Scholar
• West Patricia M., Brockett Patrick L., Golden Linda L. A comparative analysis of neural networks and statistical methods for predicting consumer choice. Marketing Sci. (1997) 16(4):370–391Link, Google Scholar
• West Patricia M., Ariely Dan, Bellman Steve, Bradlow Eric, Huber Joel, Johnson Eric, Kahn Barbara, Little John D. C., Schkade David. Agents to the rescue? Marketing Lett. (1999) 10(3):285–300Crossref, Google Scholar
• Weston Jason, Watkins Chris, Verleysen Michel. Support vector machines for multi-class pattern recognition. Proc. 6th Eur. Sympos. Artificial Neural Networks (ESANN) (1998) (Bruges, Belgium) 276–288Google Scholar
• Weston Jason, Gammerman Alex, Stitson Mark O., Vapnik Vladimir, Vovk Volodya, Watkins Chris, Schölkopf Bernhard, Burges Christopher J. C., Smola Alexander J. Support vector density estimation. Advances in Kernel Methods—Support Vector Learning (1999) (MIT Press, Cambridge, MA) 293–306Google Scholar
• Wierenga B., van Bruggen G. H.Marketing Management Support Systems: Principles, Tools and Implementation (2000) (Kluwer Academic Publishing, Boston, MA) Crossref, Google Scholar
• Wierenga Beremd, van Bruggen Gerrit H., Staelin Richard. The success of marketing management support systems. Marketing Sci. (1999) 18(3):196–207Link, Google Scholar
• Young Gale, Householder A. S. Factorial invariance and significance. Psychometrika (1940) 5:47–56Crossref, Google Scholar