dc.creator | Becker C.O. | |
dc.creator | Ferreira P.A.V. | |
dc.date | 2013 | |
dc.date | 2015-06-25T19:12:48Z | |
dc.date | 2015-11-26T15:10:07Z | |
dc.date | 2015-06-25T19:12:48Z | |
dc.date | 2015-11-26T15:10:07Z | |
dc.date.accessioned | 2018-03-28T22:20:19Z | |
dc.date.available | 2018-03-28T22:20:19Z | |
dc.identifier | | |
dc.identifier | Proceedings - 2013 12th International Conference On Machine Learning And Applications, Icmla 2013. Ieee Computer Society, v. 2, n. , p. 339 - 344, 2013. | |
dc.identifier | | |
dc.identifier | 10.1109/ICMLA.2013.145 | |
dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-84899461109&partnerID=40&md5=8ac106019099d58c5be4cd499e8a7d37 | |
dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/88844 | |
dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/88844 | |
dc.identifier | 2-s2.0-84899461109 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1257982 | |
dc.description | Semi-supervised learning can be defined as the ability to improve the predictive performance of an algorithm by providing it with data which hasn't been previously labeled. Manifold Regularization is a semi-supervised learning approach that extends the regularization framework so as to include additional regularization penalties that are based on the graph Laplacian as the empirical estimator of the underlying manifold. The incorporation of such terms rely on additional hyper-parameters, which, together with the original kernel and regularization parameters, are known to influence algorithm behavior. This paper proposes a gradient approach to the optimization of such hyper-parameters which is based on the closed form for the generalized cross validation estimate, being valid when the learning optimality conditions can be represented as a linear system, such as is the case for Laplacian Regularized Least Squares. For the subset hyper-parameters that are integer quantities, as is the case for the Laplacian matrix hyper-parameters, we propose the optimization of the weight components of a sum of base terms. Results of computational experiments are presented to illustrate the technique proposed. © 2013 IEEE. | |
dc.description | 2 | |
dc.description | | |
dc.description | 339 | |
dc.description | 344 | |
dc.description | Association for Machine Learning and Applications (AML and A),IEEE Computer Society | |
dc.description | Chapelle, O., Scholkopf, B., Zien, A., (2006) Semi-Supervised Learning, , Eds Cambridge MA MIT Press | |
dc.description | Belkin, M., Niyogi, P., Sindhwani, V., Manifold regularization: A geometric framework for learning from labeled and unlabeled examples (2006) The Journal of Machine Learning Research, 7, pp. 2399-2434 | |
dc.description | Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S., Choosing multiple parameters for support vector machines (2002) Machine Learning, 46 (1-3), pp. 131-159 | |
dc.description | Friedrichs, F., Igel, C., Evolutionary tuning of multiple svm parameters (2005) Neurocomputing, 64, pp. 107-117 | |
dc.description | Bennett, K.P., Hu, J., Ji, X., Kunapuli, G., Pang, J.-S., Model selection via bilevel optimization (2006) Neural Networks 2006. IJCNN?06. International Joint Conference on, pp. 1922-1929. , IEEE | |
dc.description | Cawley, G.C., Leave-one-out cross-validation based model selection criteria for weighted ls-svms (2006) Neural Networks 2006. IJCNN?06. International Joint Conference on, pp. 1661-1668. , http://theoval.cmp.uea.ac.uk/matlab/on16-Jul-2013, IEEE software retrieved from | |
dc.description | Keerthi, S., Sindhwani, V., Chapelle, O., An efficient method for gradient-based adaptation of hyperparameters in svm models (2007) NIPS 2006 | |
dc.description | Moore, G., Bergeron, C., Bennett, K.P., Model selection for primal svm (2011) Machine Learning, 85 (1-2), pp. 175-208 | |
dc.description | Scholkopf, B., Herbrich, R., Smola, A.J., A generalized representer theorem (2001) Computational Learning Theory, pp. 416-426. , Springer | |
dc.description | Cawley, G.C., Talbot, N.L., Fast exact leave-one-out crossvalidation of sparse least-squares support vector machines (2004) Neural Networks, 17 (10), pp. 1467-1476 | |
dc.description | Rifkin, R.M., Everything old is new again: A fresh look at historical approaches in machine learning (2002) Ph.D. Dissertation, , Massachussetts Institute of Technology | |
dc.description | Rifkin, R.M., Lippert, R.A., (2007) Notes on Regularized Least Squares | |
dc.description | Pahikkala, T., Boberg, J., Salakoski, T., Fast n-fold cross-validation for regularized least-squares (2006) Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), pp. 83-90. , Citeseer | |
dc.description | Allen, D.M., The relationship between variable selection and data agumentation and a method for prediction (1974) Technometrics, 16 (1), pp. 125-127 | |
dc.description | Yuan, J., Liu, X., Liu, C.-L., Leave-one-out manifold regularization (2012) Expert Systems with Applications, 39 (5), pp. 5317-5324 | |
dc.description | Nelder, J.A., Mead, R., A simplex method for function minimization (1965) Computer Journal, 7, pp. 308-313 | |
dc.description | Gonen, M., Alpaydn, E., Multiple kernel learning algorithms (2011) Journal of Machine Learning Research, 12, pp. 2211-2268 | |
dc.description | Geng, B., Xu, C., Tao, D., Yang, Y., Hua, X.-S., Ensemble manifold regularization (2009) Computer Vision and Pattern Recognition 2009. CVPR 2009, pp. 2396-2402. , IEEE Conference on | |
dc.description | Cawley, G.C., Talbot, N.L., Preventing over-fitting during model selection via bayesian regularisation of the hyper-parameters (2007) The Journal of Machine Learning Research, 8, pp. 841-861 | |
dc.description | Sindhwani, V., Niyogi, P., Belkin, M., Beyond the point cloud: From transductive to semi-supervised learning (2005) Proceedings of the 22nd International Conference on Machine Learning, pp. 824-831. , ACM | |
dc.description | Melacci, S., Belkin, M., Laplacian support vector machines trained in the primal (2011) Journal of Machine Learning Research, 12, pp. 1149-1184 | |
dc.description | Nene, S.A., Nayar, S.K., Murase, H., Columbia object image library (coil-20) (1996) Dept. Comput. Sci, 62. , http://www.cs.columbia.edu/CAVE/coil-20.html, Columbia Univ., New York. [Online] | |
dc.description | Bache, K., Lichman, M., (2013) UCI Machine Learning Repository, , http://archive.ics.uci.edu/ml, [Online]. Available | |
dc.description | (2009) Natick, Massachusetts: The MathWorks Inc., , MATLAB, version 7.8.0 (R2009a) | |
dc.description | Bazaraa, M.S., Shetty, C.M., (1979) Nonlinear Programming: Theory and Algorithms, , New York Wiley | |
dc.description | Rosenberg, D., Sindhwani, V., Bartlett, P., Niyogi, P., Multiview point cloud kernels for semisupervised learning[lecture notes] (2009) Signal Processing Magazine, IEEE, 26 (5), pp. 145-150 | |
dc.description | Minh, H.Q., Bazzani, L., Murino, V., A unifying framework for vector-valued manifold regularization and multi-view learning (2013) Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 100-108 | |
dc.description | Bergstra, J., Bengio, Y., Random search for hyper-parameter optimization (2012) The Journal of Machine Learning Research, 13, pp. 281-305 | |
dc.language | en | |
dc.publisher | IEEE Computer Society | |
dc.relation | Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 | |
dc.rights | fechado | |
dc.source | Scopus | |
dc.title | Gradient Hyper-parameter Optimization For Manifold Regularization | |
dc.type | Actas de congresos | |