Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Overall risk criterion estimation of hidden Markov model parameters
Speech Communication
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Alternate Objective Function for Markovian Fields
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Investigating loss functions and optimization methods for discriminative learning of label sequences
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
Training conditional random fields with multivariate evaluation measures
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Minimum risk annealing for training log-linear models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition
ICSC '07 Proceedings of the International Conference on Semantic Computing
Large-margin minimum classification error training: A theoretical risk minimization perspective
Computer Speech and Language
Modified MMI/MPE: a direct evaluation of the margin in speech recognition
Proceedings of the 25th international conference on Machine learning
Minimum tag error for discriminative training of conditional random fields
Information Sciences: an International Journal
Training data selection for improving discriminative training of acoustic models
Pattern Recognition Letters
Maximum Margin Training of Gaussian HMMs for Handwriting Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A robust model for on-line handwritten japanese text recognition
International Journal on Document Analysis and Recognition - Special Issue DRR09
Large margin cost-sensitive learning of conditional random fields
Pattern Recognition
Softmax-margin CRFs: training log-linear models with cost functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Error approximation and minimum phone error acoustic model estimation
IEEE Transactions on Audio, Speech, and Language Processing
ICDAR 2011 Chinese Handwriting Recognition Competition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
CASIA Online and Offline Chinese Handwriting Databases
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
Large margin hidden Markov models for speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Approximate Test Risk Bound Minimization Through Soft Margin Estimation
IEEE Transactions on Audio, Speech, and Language Processing
An approach for real-time recognition of online Chinese handwritten sentences
Pattern Recognition
Handwritten Chinese Text Recognition by Integrating Multiple Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Margin Discriminative Semi-Markov Model for Phonetic Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Minimum-risk training of approximate CRF-based NLP systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
ICDAR '13 Proceedings of the 2013 12th International Conference on Document Analysis and Recognition
Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Semi-Markov conditional random fields (semi-CRFs) are usually trained with maximum a posteriori (MAP) criterion which adopts the 0/1 cost for measuring the loss of misclassification. In this paper, based on our previous work on handwritten Chinese/Japanese text recognition (HCTR) using semi-CRFs, we propose an alternative parameter learning method by minimizing the risk on the training set, which has unequal misclassification costs depending on the hypothesis and the ground-truth. Based on this framework, three non-uniform cost functions are compared with the conventional 0/1 cost, and training data selection is incorporated to reduce the computational complexity. In experiments of online handwriting recognition on databases CASIA-OLHWDB and TUAT Kondate, we compared the performances of the proposed method with several widely used learning criteria, including conditional log-likelihood (CLL), softmax-margin (SMM), minimum classification error (MCE), large-margin MCE (LM-MCE) and max-margin (MM). On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.