TY - GEN
T1 - A review on conditional random fields as a sequential classifier in machine learning
AU - Liliana, Dewi Yanti
AU - Basaruddin, T.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - In this paper we present a comprehensive review of a well-known sequential classifier in machine learning Conditional Random Fields (CRFs). CRFs is proposed to cope the limitation of both generative Hidden Markov Models (HMMs) and discriminative Maximum Entropy Markov Models (MEMMs) for solving the sequential classification problems. CRFs is widely used to accomplish the sequential classification which has a temporal dimension. On its way, CRFs has been improved both on the structural learning model as well as on the area of implementation. Those areas are varying from information extraction, image understanding, computer vision, behavioral analysis, natural language processing, bioinformatics, etc. This review provides a compact and informative summary of the major research on CRFs. We present a brief description about CRFs fundamental, CRFs roadmap, and CRFs related area of implementation from several literature papers on CRFs. The contribution of this paper is to explore the roadmap of CRFs research and potential prospect in developing CRFs to solve machine learning problems, particularly problems with sequential structures.
AB - In this paper we present a comprehensive review of a well-known sequential classifier in machine learning Conditional Random Fields (CRFs). CRFs is proposed to cope the limitation of both generative Hidden Markov Models (HMMs) and discriminative Maximum Entropy Markov Models (MEMMs) for solving the sequential classification problems. CRFs is widely used to accomplish the sequential classification which has a temporal dimension. On its way, CRFs has been improved both on the structural learning model as well as on the area of implementation. Those areas are varying from information extraction, image understanding, computer vision, behavioral analysis, natural language processing, bioinformatics, etc. This review provides a compact and informative summary of the major research on CRFs. We present a brief description about CRFs fundamental, CRFs roadmap, and CRFs related area of implementation from several literature papers on CRFs. The contribution of this paper is to explore the roadmap of CRFs research and potential prospect in developing CRFs to solve machine learning problems, particularly problems with sequential structures.
KW - conditional random fields
KW - machine learning
KW - sequential classifier
KW - structured learning
UR - http://www.scopus.com/inward/record.url?scp=85041690417&partnerID=8YFLogxK
U2 - 10.1109/ICECOS.2017.8167121
DO - 10.1109/ICECOS.2017.8167121
M3 - Conference contribution
AN - SCOPUS:85041690417
T3 - ICECOS 2017 - Proceeding of 2017 International Conference on Electrical Engineering and Computer Science: Sustaining the Cultural Heritage Toward the Smart Environment for Better Future
SP - 143
EP - 148
BT - ICECOS 2017 - Proceeding of 2017 International Conference on Electrical Engineering and Computer Science
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Electrical Engineering and Computer Science, ICECOS 2017
Y2 - 22 August 2017 through 23 August 2017
ER -