Reading List

Support Vector Learning

[Lin02] C.-J. Lin, A comparison of methods for multiclass support vector machines, IEEE transactions on neural networks, vol. 13, no. 2, pp. 415–425, Jan. 2002.

[Joachims09] Joachims T, Hofmann T, Yue Y, Yu C-N, Predicting structured objects with support vector machines, Communications of the ACM. 2009;52(11)

Performance Evaluation

[Fawcett06] T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, Jun. 2006.

[Demsar06] J. Demsar, Statistical Comparisons of Classifiers over Multiple Data Sets, Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006.

Unsupervised Learning

[Ding08] C. Ding, X. He, H. D. Simon, and R. Jin, On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering, Lawrence Berkeley National Laboratory, 2008.

[Dhillon04] I. S. Dhillon, Y. Guan, and B. Kulis, Kernel k-means , Spectral Clustering and Normalized Cuts, in Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD' 04, 2004, pp. 551-556.

Deep Learning

[Salakhutdinov09] R. Salakhutdinov and G. Hinton, Deep Boltzmann machines, in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2009, pp. 448–455.

[Shin13] H.-C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data., IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1930–43, Aug. 2013.

Large Scale Machine Learning

[Weston10] J. Weston, S. Bengio, and N. Usunier, Large scale image annotation: learning to rank with joint word-image embeddings,” Mach. Learn., vol. 81, no. 1, pp. 21–35, Jul. 2010.

[Le12] Q. V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng, Building high-level features using large scale unsupervised learning, in Proceedings of the 29th International Conference on Machine Learning, 2012.