### Abstract

In this paper, we consider the problem to predict the class of an unknown sample after learning from queries. We propose to evaluate a learning algorithm by a loss function for the prediction under a constraint. In this paper, the error probability for the prediction and the number of queries is defined as the loss function and the constraint, respectively. Then our objective is to minimize the error probability, the error probability is determined by what presentation order for instances to query and how to predict. Since the optimal prediction has been shown in previous researches, we only have to select the optimal presentation order for instances to query. We propose a lower bound used in the branch-and-bound algorithm to select the optimal presentation order for instances. Lastly, we show the efficiency of the algorithm using the derived lower bound by numerical computation.

Original language | English |
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Title of host publication | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |

Editors | Anon |

Publisher | IEEE |

Pages | 4412-4417 |

Number of pages | 6 |

Volume | 5 |

Publication status | Published - 1997 |

Event | Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) - Orlando, FL, USA Duration: 1997 Oct 12 → 1997 Oct 15 |

### Other

Other | Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) |
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City | Orlando, FL, USA |

Period | 97/10/12 → 97/10/15 |

### Fingerprint

### ASJC Scopus subject areas

- Hardware and Architecture
- Control and Systems Engineering

### Cite this

*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics*(Vol. 5, pp. 4412-4417). IEEE.

**Learning with membership queries to minimize prediction error.** / Ukita, Yoshifumi; Matsushima, Toshiyasu; Hirasawa, Shigeichi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.*vol. 5, IEEE, pp. 4412-4417, Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5), Orlando, FL, USA, 97/10/12.

}

TY - GEN

T1 - Learning with membership queries to minimize prediction error

AU - Ukita, Yoshifumi

AU - Matsushima, Toshiyasu

AU - Hirasawa, Shigeichi

PY - 1997

Y1 - 1997

N2 - In this paper, we consider the problem to predict the class of an unknown sample after learning from queries. We propose to evaluate a learning algorithm by a loss function for the prediction under a constraint. In this paper, the error probability for the prediction and the number of queries is defined as the loss function and the constraint, respectively. Then our objective is to minimize the error probability, the error probability is determined by what presentation order for instances to query and how to predict. Since the optimal prediction has been shown in previous researches, we only have to select the optimal presentation order for instances to query. We propose a lower bound used in the branch-and-bound algorithm to select the optimal presentation order for instances. Lastly, we show the efficiency of the algorithm using the derived lower bound by numerical computation.

AB - In this paper, we consider the problem to predict the class of an unknown sample after learning from queries. We propose to evaluate a learning algorithm by a loss function for the prediction under a constraint. In this paper, the error probability for the prediction and the number of queries is defined as the loss function and the constraint, respectively. Then our objective is to minimize the error probability, the error probability is determined by what presentation order for instances to query and how to predict. Since the optimal prediction has been shown in previous researches, we only have to select the optimal presentation order for instances to query. We propose a lower bound used in the branch-and-bound algorithm to select the optimal presentation order for instances. Lastly, we show the efficiency of the algorithm using the derived lower bound by numerical computation.

UR - http://www.scopus.com/inward/record.url?scp=0031363145&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031363145&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0031363145

VL - 5

SP - 4412

EP - 4417

BT - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

A2 - Anon, null

PB - IEEE

ER -