Neural Computation
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Maximum likelihood successive state splitting
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
HMM topology optimization for handwriting recognition
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
On the structure of hidden Markov models
Pattern Recognition Letters
Short communication: Variable space hidden Markov model for topic detection and analysis
Knowledge-Based Systems
A prediction algorithm for time series based on adaptive model selection
Expert Systems with Applications: An International Journal
Distributed Continuous Action Recognition Using a Hidden Markov Model in Body Sensor Networks
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Minimal-latency human action recognition using reliable-inference
Image and Vision Computing
Semantic multi-grain mixture topic model for text analysis
Expert Systems with Applications: An International Journal
Segmental K-means learning with mixture distribution for HMM based handwriting recognition
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
Detecting latent attack behavior from aggregated Web traffic
Computer Communications
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Rule-based trajectory segmentation for modeling hand motion trajectory
Pattern Recognition
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This paper proposes a model selection criterion for classificationproblems. The criterion focuses on selecting modelsthat are discriminant instead of models based on the Occam'srazor principle of parsimony between accurate modelingand complexity. The criterion, dubbed DiscriminativeInformation Criterion (DIC), is applied to the optimizationof Hidden Markov Model topology aimed at the recognitionof cursively-handwritten digits. The results show that DIC-generatedmodels achieve 18% relative improvement in per-formancefrom a baseline system generated by the BayesianInformation Criterion (BIC).