References
- AndrewsRLAinslieACurrimISAn empirical comparison of logit choice models with discrete versus continuous representations of heterogeneityJournal of Marketing Research200239447948710.1509/jmkr.39.4.479.19124
- AndrewsRLRecovering and profiling the true segmentation structure in markets: an empirical investigationInternational Journal of Research in Marketing200320217719210.1016/S0167-8116(03)00017-X
- BaxterJA Bayesian/information theoretic model of learning to learn via multiple task samplingMachine Learning199728173910.1023/A:1007327622663
- Ben-David S and Schuller R (2003). Exploiting task relatedness for multiple task learning. In: Proceedings of the 16th Annual Conference on Computational Learning Theory, Washington, DC, USA.
- BreimanLBagging predictorsMachine Learning1996242123140
- BruzzoneLMarconciniMToward the automatic updating of land-cover maps by a domain-adaptation SVM classifier and a circular validation strategyIEEE Transactions on Geoscience and Remote Sensing20094741108112210.1109/TGRS.2008.2007741
- CaruanaRMultitask learningMachine Learning1997281417510.1023/A:1007379606734
- Dai WY, Yang Q, Xue GR and Yu R (2007). Boosting for transfer learning. In: Ghahramani Z (ed.) Proceeding of the 24th International Conference on Machine Learning. ACM Press: Corvalis, OR, pp 193–200.
- FishKEJohnsonbJDDorseybREBlodgettJGUsing an artificial neural network trained with a genetic algorithm to model brand shareJournal of Business Research2004571798510.1016/S0148-2963(02)00287-4
- FransesPHPaapRQuantitative Models in Marketing Research2001
- GreeneWHHensherDAA latent class model for discrete choice analysis: Contrasts with mixed logitTransportation Research Part B: Methodological200337868169810.1016/S0191-2615(02)00046-2
- GuadagniPMLittleJDA logit model of brand choice calibrated on scanner dataMarketing Science19832320323810.1287/mksc.2.3.203
- HastieTTibshiraniRFriedmanJThe Elements of Statistical Learning: Data mining, Inference and Prediction2001
- HuMYTsoukalasCExplaining consumer choice through neural networks: The stacked generalization approachEuropean Journal of Operational Research2003146365066010.1016/S0377-2217(02)00368-5
- HuMYShankerMZhangGPHungMSModeling consumer situational choice of long distance communication with neural networksDecision Support Systems200844489990810.1016/j.dss.2007.10.009
- Jiang J and Zhai CX (2007). Instance weighting for domain adaptation in NLP. In: Carroll J, van den Bosch A and Zaenen A (eds.) Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics: Prague, Czech Republic, pp 264–271.
- Kamishima T, Hamasaki M and Akaho S (2009). TrBagg: A simple transfer learning method and its application to personalization in collaborative tagging. In: Wang W et al (eds.) Proceeding of Ninth IEEE International Conference on Data Mining. IEEE Computer Society: Miami, FL, pp 219–228.
- LemmensACrouxCBagging and boosting classification trees to predict churnJournal of Marketing Research200643227628610.1509/jmkr.43.2.276
- LilienGLRangaswamyAMarketing Engineering: Computer-assisted Marketing Analysis and Planning2002
- PanSJYangQA survey on transfer learningIEEE Transactions on Knowledge and Data Engineering201022101345135910.1109/TKDE.2009.191
- SkinnerBFScience and Human Behavior1953
- SmithSMAlbaumGSFundamentals of Marketing Research2005
- SilverDLBennettKPGuest editor’s introduction: special issue on inductive transfer learningMachine Learning200873321522010.1007/s10994-008-5087-1
- Thrun S (1996). Is learning the N-th thing any easier than learning the first?. In: Mozer M, Jordan MI, Petsche T (eds.) Proceedings of NIPS-96, MIT Press: Denver, CO, USA, pp 640–646.
- van WezelMPotharstRImproved customer choice predictions using ensemble methodsEuropean Journal of Operational Research2007181143645210.1016/j.ejor.2006.05.029
- WestPBrockettPLGoldenLLA comparative analysis of neural networks and statistical methods for predicting consumer choiceMarketing Science199716437039110.1287/mksc.16.4.370
- WuQTanSA two-stage framework for cross-domain sentiment classificationExpert Systems with Applications201138111426914275
- Yang T, Jin R, Jain AK, Zhou Y and Tong W (2010). Unsupervised transfer classification: Application to text categorization. In: Proceedings of KDD 2010, Washington, DC, USA.