Meta-Learning: Searching in the Model Space

摘要：

There is no free lunch, no single learning algorithm that will outperform other algorithms on all data. In practice different approaches are tried and the best algorithm selected. An alternative solution is to build new algorithms on demand by creating a framework that accommodates many algorithms. The best combination of parameters and procedures is searched here in the space of all possible models belonging to the framework of Similarity-Based Methods (SBMs). Such meta-learaing approach gives a chance to find the best method in all cases. Issues related to the meta-leaming and first tests of this approach are presented.

作者: W(1)odzis(1)aw Duch Karol Grudzi(n)ski

作者单位: Department of Computer Methods, Nicholas Copernicus University,Grudziadzka 5,87-100 Toru(n), Poland

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 273-278

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）