.png){kind=icon}
Return
Efficient Exact Inference With Loss Augmented Objective in Structured Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2016.2598721.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms-the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, F-beta-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.
Keywords:
Dynamic programming
graphical models
high-order potentials
inference
margin scaling (MS)
slack scaling (SS)
structural support vector machines (SVMs)
structured output
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
ShareSaveAn Anthropogenic Habitat Facilitates the Establishment of Non-Native Birds by Providing Underexploited Resources.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveMechanical and leakrate predictions for nuclear power plant containments in accidental conditions
PREAI
ShareSaveShareSaveShareSaveShareSave
ShareSaveChemical and Kinetic Reaction Mechanisms of Quinohemoprotein Amine Dehydrogenase fromParacoccus denitrificansBiochemistry
BiochemistryIF0
PREAI
ShareSave
View more