S. Ben-David, N. Eiron, H.U. Simon, "Limitations of Learning Via Embeddings in Euclidean Half-Spaces", Journal of Machine Learning Research, Volume 3, pp. 441-- 461, 2003. 16 Home/Search Document Not in Database Summary Related Articles Check |
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....correspond to training points chosen independent and identically distributed according to some probability. Note that the universal approximation capability of SVMs does not imply that every function can be approximated with a large margin, i.e. small weight vector w. It has been shown in [2] that a large number of functions cannot be embedded such that a large margin arises. Hence another topic of research is which kernels are appropriate for which functions to be approximated such that a large margin can be obtained. ....
S. Ben-David, N. Eiron, and H. U. Simon. Limitations of Learning via Embedding in Euclidean Half Spaces. NeuroCOLT Technical Report
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Ben-David, S., Eiron, N., and Simon, U., H., " Limitations of Learning via Embeddings in Euclidean Half-Spaces" Proceedings of COLT 2001.
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S. Ben-David, N. Eiron, H.U. Simon, "Limitations of Learning Via Embeddings in Euclidean Half-Spaces", Journal of Machine Learning Research, Volume 3, pp. 441-- 461, 2003. 16
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Ben-David, S., Eiron, N., & Simon, H. U. (2002). Limitations of learning via embeddings in euclidean half spaces. J. of Mach. Learning Research, 3, 441--461. Collins, M., & Du#y, N. (2001). Convolution kernels for natural language. NIPS 14 (pp. 625--632).
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S. Ben-David, N. Eiron, and H.U. Simon. Limitations of learning via embeddings in euclidean half-spaces. In 14th Annual Conference on Computational Learning Theory, COLT 2001.
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S. Ben-David, N. Eiron, H.U. Simon, \Limitations of Learning Via Embeddings in Euclidean Half-Spaces", Journal of Machine Learning Research 3: 441-461, 2002.
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S. Ben-David, N. Eiron, and H.U. Simon. Limitations of learning via embeddings in Euclidean half spaces. Journal of Machine Learning Research, 3:441--461, 2002.
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