The following are the references for all the citations throughout the book:
Adomavicius, G. and Tuzhilin, A.. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. 2005.
Bondu, A., Lemaire, V., Boulle, M.. Exploration vs. exploitation in active learning: A Bayesian approach. The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain. 2010.
Breunig, M. M., Kriegel, H.-P., Ng, R. T., Sander, J.. LOF: Identifying Density-based Local Outliers (PDF). Proceedings from the 2000 ACM SIGMOD International Conference on Management of Data, 29(2), 93–104. 2000
Dror, G., Boulle ́, M., Guyon, I., Lemaire, V., and Vogel, D.. The 2009 Knowledge Discovery in Data Competition (KDD Cup 2009) Volume 3, Challenges in Machine Learning, Massachusetts, US. Microtome Publishing. 2009.
Gelman, A. and Nolan, D.. Teaching Statistics a bag of tricks. Cambridge, MA. Oxford University Press. 2002.
Goshtasby, A. A. Image Registration Principles, Tools and Methods. London, Springer. 2012.
Koller, D. and Friedman, N.. Probabilistic Graphical Models Principles and Techniques. Cambridge, Mass. MIT Press. 2012.
Kurucz, M., Siklósi, D., Bíró, I., Csizsek, P., Fekete, Z., Iwatt, R., Kiss, T., and Szabó, A.. KDD Cup 2009 @ Budapest: feature partitioning and boosting 61. JMLR W&CP 7, 65–75. 2009.
Mariscal, G., Marban, O., and Fernandez, C.. A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review, 25(2), 137–166. 2010.
Mew, K. (2015). Android 5 Programming by Example. Birmingham, UK. Packt Publishing.
Miller, H., Clarke, S., Lane, S., Lonie, A., Lazaridis, D., Petrovski, S., and Jones, O.. Predicting customer behavior: The University of Melbourne's KDD Cup report, JMLR W&CP 7, 45–55. 2009.
Niculescu-Mizil, A., Perlich, C., Swirszcz, G., Sind- hwani, V., Liu, Y., Melville, P., Wang, D., Xiao, J., Hu, J., Singh, M., Shang, W. X., and Zhu, Y. F.. Winning the KDD Cup Orange Challenge with Ensemble Selection. JMLR W&CP, 7, 23–34. 2009. Retrieved from http://jmlr.org/proceedings/papers/v7/niculescu09/niculescu09.pdf.
Osugi, T., Deng, K., and Scott, S.. Balancing exploration and exploitation: a new algorithm for active machine learning. Fifth IEEE International Conference on Data Mining, Houston, Texas. 2005.
Tsai, J., Kaminka, G., Epstein, S., Zilka, A., Rika, I., Wang, X., Ogden, A., Brown, M., Fridman, N., Taylor, M., Bowring, E., Marsella, S., Tambe, M., and Sheel, A.. ESCAPES - Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison. Proceedings from 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011) 2 (6), 457–464. 2011. Retrieved from http://www.aamas-conference.org/Proceedings/aamas2011/papers/D3_G57.pdf.
Tsanas, A. and Xifara. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49, 560-567. 2012.
Utts, J.. What Educated Citizens Should Know About Statistics and Probability. The American Statistician, 57 (2), 74-79. 2003.
Wallach, H. M., Murray, I., Salakhutdinov, R., and Mimno, D.. Evaluation Methods for Topic Models. Proceedings from the 26th International conference on Machine Learning, Montreal, Canada. 2009. Retrieved from http://mimno.infosci.cornell.edu/papers/wallach09evaluation.pdf.
Witten, I. H. and Frank, E.. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. USA. Morgan Kaufmann Publishers. 2000.
Xie, J., Rojkova, V., Pal, S., and Coggeshall, S.. A Combination of Boosting and Bagging for KDD Cup 2009. JMLR W&CP, 7, 35–43. 2009.
Ziegler, C-N., McNee, S. M., Konstan, J. A., and Lausen, G.. Improving Recommendation Lists Through Topic Diversification. Proceedings from the 14th International World Wide Web Conference (WWW '05), Chiba, Japan. 2005. Retrieved from http://www2.informatik.uni-freiburg.de/~cziegler/papers/WWW-05-CR.pdf.