Training MT Model Using Structural SVM

2010 ◽  
Author(s):  
Tiansang Du ◽  
Baobao Chang
Keyword(s):  
Structural Svm

Exploring overall opinions for document level sentiment classification with structural SVM

2017 ◽  
Vol 25 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Xiaojia Pu ◽  
Gangshan Wu ◽  
Chunfeng Yuan
Keyword(s):  
Sentiment Classification ◽  
Structural Svm ◽  
Document Level

Sentiment Analysis using Latent Structural SVM

2016 ◽  
Vol 22 (5) ◽  
pp. 240-245 ◽  
Author(s):  
Seung-Won Yang ◽  
Changki Lee
Keyword(s):  
Sentiment Analysis ◽  
Structural Svm

scholarly journals Structural SVM with Partial Ranking for Activity Segmentation and Classification

2015 ◽  
Vol 22 (12) ◽  
pp. 2344-2348 ◽  
Author(s):  
Guopeng Zhang ◽  
Massimo Piccardi
Keyword(s):  
Partial Ranking ◽  
Structural Svm

Kernelized structural SVM learning for supervised object segmentation

CVPR 2011 ◽  
2011 ◽  
Author(s):  
Luca Bertelli ◽  
Tianli Yu ◽  
Diem Vu ◽  
Burak Gokturk
Keyword(s):  
Object Segmentation ◽  
Structural Svm

scholarly journals A Structural SVM Based Approach for Binary Classification under Class Imbalance

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Fan Cheng ◽  
Kang Yang ◽  
Lei Zhang
Keyword(s):  
Performance Measures ◽  
Binary Classification ◽  
Empirical Studies ◽  
Class Imbalance ◽  
Loss Functions ◽  
Misclassification Error ◽  
Structural Svm ◽  
Learning Techniques ◽  
Wide Range ◽  
Machine Learning Applications

Class imbalance situations, where one class is rare compared to the other, arise frequently in machine learning applications. It is well known that the usual misclassification error is not suitable in such settings. A wide range of performance measures such as AM and QM have been proposed for this problem. However, due to computational difficulties, few learning techniques have been developed to directly optimize for AM or QM metric. To fill the gap, in this paper, we present a general structural SVM framework for directly optimizing AM and QM. We define the loss functions oriented to AM and QM, respectively, and adopt the cutting plane algorithm to solve the outer optimization. For the inner problem of finding the most violated constraint, we propose two efficient algorithms for the AM and QM problem. Empirical studies on the various imbalanced datasets justify the effectiveness of the proposed approach.

Sequential Labeling With Structural SVM Under Nondecomposable Losses

2018 ◽  
Vol 29 (9) ◽  
pp. 4177-4188 ◽  
Author(s):  
Guopeng Zhang ◽  
Massimo Piccardi ◽  
Ehsan Zare Borzeshi
Keyword(s):  
Structural Svm ◽  
Sequential Labeling
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