Mitchell, A., Sche er, T., Sharma, A., & Stephan, F. (1999). The VC-dimension of subclasses of pattern languages. In O. Watanabe & T. Yokomori (Eds.), Proceedings of the Tenth International Conference on Algorithmic Learning Theory, Lecture Notes in Arti cial Intelligence 1720 (pp. 89-100). Berlin: Springer-Verlag. Home/Search Document Details and Download
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Stochastic Finite Learning of the Pattern Languages - Rossmanith (Correct)
....be learned from positive data. Moreover, learning algorithms for pattern languages have already found interesting applications in a variety of domains such as molecular biology and data bases (cf. e.g. Salomaa [22, 23] and Shinohara and Arikawa [25] for an overview) Recently, Mitchell et al. [16] have shown that even the class of all one variable pattern languages has infinite VC dimension. Consequently, even this special subclass of PAT is not uniformly PAC learnable. Moreover, Schapire [24] has shown that pattern languages are not PAC learnable in the generalized model provided P=poly ....
Mitchell, A., Scheffer, T., Sharma, A., & Stephan, F. (1999). The VC-dimension of subclasses of pattern languages. In O. Watanabe & T. Yokomori (Eds.), Proceedings of the Tenth International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1720 (pp. 93--105). Berlin: Springer-Verlag.
Stochastic Finite Learning of the Pattern Languages - Rossmanith, Zeugmann (1999) (Correct)
....be learned from positive data. Moreover, learning algorithms for pattern languages have already found interesting applications in a variety of domains such as molecular biology and data bases (cf. e.g. Salomaa [22, 23] and Shinohara and Arikawa [25] for an overview) Recently, Mitchell et al. [16] have shown that even the class of all one variable pattern languages has infinite VC dimension. Consequently, even this special subclass of PAT is not uniformly PAC learnable. Moreover, Schapire [24] has shown that pattern languages are not PAC learnable in the generalized model provided P=poly ....
Mitchell, A., Scheffer, T., Sharma, A., & Stephan, F. (1999). The VC-dimension of subclasses of pattern languages. In O. Watanabe & T. Yokomori (Eds.), Proceedings of the Tenth International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1720 (pp. 89--100). Berlin: Springer-Verlag.
Stochastic Finite Learning of the Pattern Languages - Rossmanith, Zeugmann (1999) (Correct)
....be learned from positive data. Moreover, learning algorithms for pattern languages have already found interesting applications in a variety of domains such as molecular biology and data bases (cf. e.g. Salomaa [22, 23] and Shinohara and Arikawa [25] for an overview) Recently, Mitchell et al. [16] have shown that even the class of all one variable pattern languages has in nite VC dimension. Consequently, even this special subclass of PAT is not uniformly PAC learnable. Moreover, Schapire [24] has shown that pattern languages are not PAC learnable in the generalized model provided P=poly ....
Mitchell, A., Sche er, T., Sharma, A., & Stephan, F. (1999). The VC-dimension of subclasses of pattern languages. In O. Watanabe & T. Yokomori (Eds.), Proceedings of the Tenth International Conference on Algorithmic Learning Theory, Lecture Notes in Articial Intelligence 1720 (pp. 89-100). Berlin: Springer-Verlag.
Stochastic Finite Learning of The Pattern Languages - Peter Rossmanith Rossmani (Correct)
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Mitchell, A., Sche er, T., Sharma, A., & Stephan, F. (1999). The VC-dimension of subclasses of pattern languages. In O. Watanabe & T. Yokomori (Eds.), Proceedings of the Tenth International Conference on Algorithmic Learning Theory, Lecture Notes in Arti cial Intelligence 1720 (pp. 89-100). Berlin: Springer-Verlag.
An Average-Case Optimal One-Variable Pattern Language Learner - Reischuk, Zeugmann (2000) (1 citation) (Correct)
No context found.
A. Mitchell, T. Scheffer, A. Sharma and F. Stephan, The VC-dimension of subclasses of pattern languages, in "Proceedings, 10th International Conference on Algorithmic Learning Theory," (O. Watanabe, Ed.), Lecture Notes in Artificial Intelligence, Vol. xxxx, pp. xx--xx, Springer-Verlag, Berlin, 1999.
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