Randomization tests
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An Infeasible Point Method for Minimizing the Lennard-JonesPotential
Computational Optimization and Applications
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Using support vector machines for time series prediction
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
Support vector regression with ANOVA decomposition kernels
Advances in kernel methods
Support vector density estimation
Advances in kernel methods
Kernel principal component analysis
Advances in kernel methods
Further results on the margin distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Shrinking the tube: a new support vector regression algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Success of Marketing Management Support Systems
Marketing Science
An introduction to variable and feature selection
The Journal of Machine Learning Research
Bayesian Statistics and Marketing
Marketing Science
Dynamic Conversion Behavior at E-Commerce Sites
Management Science
Resampling Methods: A Practical Guide to Data Analysis
Resampling Methods: A Practical Guide to Data Analysis
Generalized Robust Conjoint Estimation
Marketing Science
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Estimating Heterogeneous EBA and Economic Screening Rule Choice Models
Marketing Science
Expert Systems with Applications: An International Journal
Generalized Robust Conjoint Estimation
Marketing Science
Estimating Heterogeneous EBA and Economic Screening Rule Choice Models
Marketing Science
Expert Systems with Applications: An International Journal
Detecting stock-price manipulation in an emerging market: The case of Turkey
Expert Systems with Applications: An International Journal
SKU demand forecasting in the presence of promotions
Expert Systems with Applications: An International Journal
Commentary---Relevancy Is Robust Prediction, Not Alleged Realism
Marketing Science
Marketing Optimization in Retail Banking
Interfaces
Improved response modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications: An International Journal
Machine learning approach for finding business partners and building reciprocal relationships
Expert Systems with Applications: An International Journal
Two New Prediction-Driven Approaches to Discrete Choice Prediction
ACM Transactions on Management Information Systems (TMIS)
Uniformly subsampled ensemble (USE) for churn management: Theory and implementation
Expert Systems with Applications: An International Journal
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Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining. The support vector machine (SVM) is a semiparametric technique with origins in the machine-learning literature of computer science. Its approach to prediction differs markedly from that of standard parametric models. We explore these differences and benchmark the SVM's prediction hit-rates against those from the multinomial logit model. Because there are few applications of the SVM in marketing, we develop a framework to position it against current modeling techniques and to assess its weaknesses as well as its strengths.