TY - GEN
T1 - On the sample complexity of consistent learning with one-sided error
AU - Takimoto, Eiji
AU - Maruoka, Akira
PY - 1993/1/1
Y1 - 1993/1/1
N2 - Although consistent learning is sufficient for PAC-learning, it has not been found what strategy makes learning more efficient, especially on the sample complexity, i.e., the number of examples required. For the first step towards this problem, only classes that have consistent learning algorithms with one-sided error are considered. A combinatorial quantity called maximal particle sets is introduced, and an upper bound of the sample complexity of consistent learning with one-sided error is obtained in terms of maximal particle sets. For the class of n-dimensional parallel axis rectangles, one of those classes that are consistently learnable with one-sided error, the cardinality of the maximal particle set is estimated and (Formula Found) upper bound of the learning algorithm for the class is obtained. This bound improves the bounds due to Blumer et al. [2] and meets the lower bound within a constant factor.
AB - Although consistent learning is sufficient for PAC-learning, it has not been found what strategy makes learning more efficient, especially on the sample complexity, i.e., the number of examples required. For the first step towards this problem, only classes that have consistent learning algorithms with one-sided error are considered. A combinatorial quantity called maximal particle sets is introduced, and an upper bound of the sample complexity of consistent learning with one-sided error is obtained in terms of maximal particle sets. For the class of n-dimensional parallel axis rectangles, one of those classes that are consistently learnable with one-sided error, the cardinality of the maximal particle set is estimated and (Formula Found) upper bound of the learning algorithm for the class is obtained. This bound improves the bounds due to Blumer et al. [2] and meets the lower bound within a constant factor.
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U2 - 10.1007/3-540-57370-4_53
DO - 10.1007/3-540-57370-4_53
M3 - Conference contribution
AN - SCOPUS:85029423545
SN - 9783540573708
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 278
BT - Algorithmic Learning Theory - 4th International Workshop, ALT 1993, Proceedings
A2 - Jantke, Klaus P.
A2 - Kobayashi, Shigenobu
A2 - Tomita, Etsuji
A2 - Yokomori, Takashi
PB - Springer Verlag
T2 - 4th Workshop on Algorithmic Learning Theory, ALT 1993
Y2 - 8 November 1993 through 10 November 1993
ER -