Commenced in January 2007
Paper Count: 30172
Modeling Language for Machine Learning
Abstract:For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058387Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1239
 Abney, S. (2002). Bootstrapping. The 40th Annual Meeting of the Association for Computational Linguistics.
 Allison, L. (2003). Types and Classes of Machine Learning and Data Mining. Twenty-Six Australasian Computer Science Conference (ACSC2003), pp.207-215, Australia.
 Bartlett, P. L., Collins, M., McAllester, D., and Taskar, B. (2004). Large margin methods for structured classification: Exponentiated Gradient algorithms and PAC-Bayesian generalization bounds. NIPS Conference.
 Cristianini, N., Shawe-Taylor, J. (2000). Introduction to Support Vector Machines. Cambridge University Press.
 Jaakkola, T. (2000) Tutorial on Variational Approximation Method. In Advanced Mean Field Methods: Theory and Practice, MIT Press.
 Lafferty, J., McCallum, A., Pereira, F. (2001). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. International Conference on Machine Learning (ICML).
 Langford, J., Beygelzimer, A. (2002). Sensitive Error Correcting Output Codes.
 Mitchell, T. (1997). Machine Learning. McGraw Hills.
 Okita, T., Manderick, B. (2003). Support Vector Learning in Distributed Environments (poster), Conference On Learning Theory and Kernel Machines, Washington.
 Rabiner, L. R. (1989) A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, VOL. 77, No. 2, February 1989.
 Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J. (2000). Support Vector Method for Novelty Detection. In Neural Information Processing Systems.
 Shawe-Taylor, J., Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.