Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge

Azencott, C. A., Ksikes, A., Swamidass, S. J., Chen, J. H., Ralaivola, L., & Baldi, P. (2007). One-to-four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties. J. Chem. Inf. Model.http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/ci600397p Baldi, 2001 Bekkerman, R., El-Yaniv, R., Tishby, N., & Winter, Y. (2003). Distributional word clusters vs words for text categorization. Code available at http://www.cs.technion.ac.il/~ronb/ Blackard, J. A., & Dean, D. J. (1998). Forest cover type. http://kdd.ics.uci.edu/databases/covertype/covertype.html Boullé, 2007, Compression-based averaging of selective naive bayes classifiers, JMLR, 1659 Boullé, 2007, Report on preliminary experiments with data grid models in the agnostic learning vs. prior knowledge challenge Cawley, 2007, Agnostic learning versus prior knowledge in the design of kernel machines Cawley, 2007, Preventing over-fitting during model selection using Bayesian regularisation, JMLR, 8, 841 Collins, J. M. Associate Director. (1999). The DTP AIDS antiviral screen program. http://dtp.nci.nih.gov/docs/aids/aids_data.html Escalante, H. J. (2007). Particle swarm model selection. JMLR [submitted for publication] Escalante, 2007, PSMS for neural networks: Results on the IJCNN 2007 agnostic vs. prior knowledge challenge Guyon, I. (2005). Datasets for the agnostic learning vs. prior knowledge competition. Technical report. Clopinet http://clopinet.com/isabelle/Projects/agnostic/Dataset.pdf Guyon, 2005, Result analysis of the nips 2003 feature selection challenge, vol. 17, 545 Guyon, I., Saffari, A., Dror, G., & Buhmann, J. (2006). Performance prediction challenge. In IEEE/INNS conference IJCNN 2006 Guyon, 2007, Agnostic vs. prior knowledge challenge Kohavi, R., & Becker, B. (1994). The Adult database. ftp://ftp.ics.uci.edu/pub/machine-learning-databases/adult/ LeCun, Y., & Cortes, C. (1998). The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/ Lutz, R. W. (2006). Logitboost with trees applied to the WCCI 2006 performance prediction challenge datasets. In Proc. IJCNN06 (pp. 2966–2969) INNS/IEEE. http://stat.ethz.ch/~lutz/publ/WCCIlogitboost.php Mitchell, T. (1999). The 20 Newsgroup dataset. http://kdd.ics.uci.edu/databases/20newsgroups/20newsgroups.html Nikulin, 2007, Non-voting classification with random sets and boosting Pranckeviciene, 2007, Feature/model selection by the linear programming SVM combined with state-of-art classifiers: What can we learn about the data Reunanen, 2007, Model selection and assessment using cross-indexing Reunanen, J. (2007). Resubstitution error is useful for guiding feature selection. JMLR [submitted for publication] Saffari, A., & Guyon, I. (2006). Quick start guide for CLOP. Technical report. Graz University of Technology and Clopinet. http://ymer.org/research/files/clop/QuickStartV1.0.pdf Simard, 2003, Best practice for convolutional neural networks applied to visual document analysis, 958 Weston, J., Elisseeff, A., Bakir, G., & Sinz, F. (2005). The Spider machine learning toolbox. http://www.kyb.tuebingen.mpg.de/bs/people/spider/ Wichard, 2007, Agnostic learning with ensembles of classifiers