Helix Partners

Over the next few weeks, we’d like to discuss some of the interesting papers we’ve come across since our last update to the reference database.  For this post, we’ve added citations to the database for 28 new papers on classification techniques.

One of the themes in this selection of papers is multilayered classifier architecture, where an algorithm is really the results of cascading modules.  A particularly compelling example is described in Pradip Ghanty’s contribution to the 10th volume of the Journal of Machine Learning.  The paper proposes the NEUROSVM architecture, which glues together a MLP neural net for feature extraction and a SVM for classification.  In the process, the algorithm significantly reduces the impact of the choice of kernel on the SVM performance.  While Helix has been using neural nets, SVMs, and even hybrid structures of the two for a while, this installment of citations includes our first introduction to the closely related work of Yann LeCun on convolutional networks.  His research represents a potential future avenue of investigation for our team.

Other highlights in this set of updates include a paper that introduces a Mahalanobis distance metric for k-nearest neighbor models (Weinberger 2009) and the presentation of a novel framework called Prototype Ranking in Yan and Ling, 2007.

Read past the break for citations for a few of the most interesting papers, or continue to references for the entire set.

1. Pradip Ghanty, Samrat Paul, and Nikhil R. Pal, “NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM,” Journal of Machine Learning Research 10 (March 2009): 591-622.
2. Fu Jie Huang and Yann LeCun, “Large-scale Learning with SVM and Convolutional Nets for Generic Object Categorization,” March 2006.
3. Patrice Simard et al., “Boxlets: a Fast Convolution Algorithm for Signal Processing and Neural Networks.”
4. Kilian Q. Weinberger and Lawrence K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classification,” Journal of Machine Learning Research 10 (February 2009): 207-244.
5. Robert J. Yan and Charles X. Ling, “Machine Learning for Stock Selection,” August 2007.