: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data.
: Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data.
: Pseudo Conditional Random Fields: Joint Training Approach to Segmenting and Labeling Sequence Data.
: High-Performance Training of Conditional Random Fields for Large-Scale Applications of Labeling Sequence Data.
: Embedding Topic Discovery in Conditional Random Fields Model for Segmenting Nuclei Using Multispectral Data.
: Analyzing Sequence Data Based on Conditional Random Fields with Co-training.
: Hidden Dynamic Probabilistic Models for Labeling Sequence Data.
: The Infinite-Order Conditional Random Field Model for Sequential Data Modeling.
: The echo state conditional random field model for sequential data modeling.
: Detecting Copy Number Variations from Array CGH Data Based on a Conditional Random Field Model.
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