Relational Transformation-based Tagging for Activity Recognition

The ability to recognize human activities from sensory information is essential for developing the next generation of smart devices. Many human activity recognition tasks are - from a machine learning perspective - quite similar to tagging tasks in natural language processing. Motivated by this similarity, we develop a relational transformation-based tagging system based on inductive logic programming principles, which is able to cope with expressive relational representations as well as a background theory. The approach is experimentally evaluated on two activity recognition tasks and an information extraction task, and compared to Hidden Markov Models, one of the most popular and successful approaches for tagging.

[1]  Luc Dehaspe,et al.  Transformation-based learning meets frequent pattern discovery , 1999 .

[2]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[3]  Thomas G. Dietterich.  Machine Learning for Sequential Data: A Review , 2002, SSPR/SPR.

[4]  Sean R. Eddy,et al.  Biological sequence analysis: Contents , 1998 .

[5]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[6]  Mark Craven,et al.  Hierarchical Hidden Markov Models for Information Extraction , 2003, IJCAI.

[7]  L. De Raedt,et al.  Logical Hidden Markov Models , 2011, J. Artif. Intell. Res..

[8]  Luc De Raedt,et al.  Relational Sequence Learning , 2008, Probabilistic Inductive Logic Programming.

[9]  Mika Raento,et al.  ContextPhone: a prototyping platform for context-aware mobile applications , 2005, IEEE Pervasive Computing.

[10]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[11]  Matthai Philipose,et al.  Common Sense Based Joint Training of Human Activity Recognizers , 2007, IJCAI.

[12]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[13]  Luc De Raedt,et al.  r-grams: Relational Grams , 2007, IJCAI.

[14]  Kristian Kersting,et al.  TildeCRF: Conditional Random Fields for Logical Sequences , 2006, ECML.

[15]  T. Speed,et al.  Biological Sequence Analysis , 1998 .

[16]  Eric Brill,et al.  Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging , 1995, CL.

[17]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[18]  Matthai Philipose,et al.  Maximum A Posteriori Path Estimation with Input Trace Perturbation: Algorithms and Application to Credible Rating of Human Routines , 2005, IJCAI.