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Authors: Carsten Elfers ; Hartmut Messerschmidt and Otthein Herzog

Affiliation: University Bremen, Germany

Keyword(s): Approximate feature functions, Conditional random fields, Partially matching feature functions, Regularization.

Abstract: Conditional Exponential Models (CEM) are effectively used in several machine learning approaches, e.g., in Conditional Random Fields. Their feature functions are typically either satisfied or not. This paper presents a way to use partially matching feature functions which are satisfied to some degree and corresponding issues while training. Using partially matching feature functions improves the inference accuracy in domains with sparse reference data and avoids overfitting. Unfortunately, the typically used Maximum Likelihood training includes some issues for using partially matching feature functions. In this context three problems (inequality of influence, unlimited weight boundaries and local optima in parameter space) with Improved Iterative Scaling (a popular training algorithm for Conditional Exponential Models) using such feature functions are stated and solved.

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Paper citation in several formats:
Elfers, C.; Messerschmidt, H. and Herzog, O. (2012). ISSUES WITH PARTIALLY MATCHING FEATURE FUNCTIONS IN CONDITIONAL EXPONENTIAL MODELS. In Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART 2012) - Volume 2: SSML; ISBN 978-989-8425-95-9; ISSN 2184-433X, SciTePress, pages 571-578. DOI: 10.5220/0003855205710578

@conference{ssml12,
author={Carsten Elfers. and Hartmut Messerschmidt. and Otthein Herzog.},
title={ISSUES WITH PARTIALLY MATCHING FEATURE FUNCTIONS IN CONDITIONAL EXPONENTIAL MODELS},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART 2012) - Volume 2: SSML},
year={2012},
pages={571-578},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003855205710578},
isbn={978-989-8425-95-9},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART 2012) - Volume 2: SSML
TI - ISSUES WITH PARTIALLY MATCHING FEATURE FUNCTIONS IN CONDITIONAL EXPONENTIAL MODELS
SN - 978-989-8425-95-9
IS - 2184-433X
AU - Elfers, C.
AU - Messerschmidt, H.
AU - Herzog, O.
PY - 2012
SP - 571
EP - 578
DO - 10.5220/0003855205710578
PB - SciTePress

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