Relational sequence learning

被引：3

作者：

Kersting, Kristian ^{[1
]}

De Raedt, Luc ^{[2
]}

Gutmann, Bernd ^{[2
]}

Karwath, Andreas ^{[3
]}

Landwehr, Niels ^{[3
]}

机构：

[1] CSAIL, Massachusetts Institute of Technology, Cambridge, MA 02139-4307

[2] Departement Computerwetenschappen, K.U. Leuven, Heverlee B-3001

[3] Machine Learning Lab., Institute for Computer Science, University of Freiburg, Freiburg 79110, Georges-Koehler Allee

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2008年 / 4911 LNAI卷

关键词：

Markov processes - Reinforcement learning;

D O I：

10.1007/978-3-540-78652-8_2

中图分类号：

学科分类号：

摘要：

Sequential behavior and sequence learning are essential to intelligence. Often the elements of sequences exhibit an internal structure that can elegantly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a combinatorial explosion of the model complexity. This chapter briefly reviews relational sequence learning and describes several techniques tailored towards realizing this, such as local pattern mining techniques, (hidden) Markov models, conditional random fields, dynamic programming and reinforcement learning. © 2008 Springer-Verlag Berlin Heidelberg.

引用

页码：28 / 55

页数：27

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