Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference68 articles.
1. Anguita, D., Ghio, A., Greco, N., Oneto, L., & Ridella, S. (2010). Model selection for support vector machines: Advantages and disadvantages of the machine learning theory. In International joint conference on neural networks (pp. 1–8).
2. Anguita, D., Ghio, A., Oneto, L., & Ridella, S. (2011). The impact of unlabeled patterns in rademacher complexity theory for kernel classifiers. In: Advances in neural information processing systems, (pp. 585–593).
3. Anguita, D., Ghio, A., Oneto, L., & Ridella, S. (2011). In-sample model selection for support vector machines. In International joint conference on neural networks (pp. 1154–1161).
4. Anguita, D., Ghio, A., Oneto, L., & Ridella, S. (2012). In-sample and out-of-sample model selection and error estimation for support vector machines. IEEE Transactions on Neural Networks and Learning Systems, 23(9), 1390–1406.
5. Anthony, M. (2001). Discrete mathematics of neural networks: Selected topics. Philadelphia: Society for Industrial Mathematics.
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