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http://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2016/Lecture/Structured%20SVM.pdf
Structured Learning •We need a more powerful function f •Input and output are both objects with structures •Object: sequence, list, tree, bounding box … X is the space of one kind of object Y is the space of another kind of object
https://www.youtube.com/watch?v=B6DNve44Hmg
Jul 15, 2013 · The Image Analysis Class 2013 by Prof. Fred Hamprecht. It took place at the HCI / Heidelberg University during the summer term of 2013. Part 03 -- Structured Support Vector Machine (structSVM ...Author: UniHeidelberg
https://www.sciencedirect.com/science/article/pii/S0893608017300321
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer.Cited by: 3
http://www.cs.cornell.edu/people/tj/publications/tsochantaridis_etal_04a.pdf
Support Vector Machine Learning for Interdependent and Structured Output Spaces Ioannis Tsochantaridis [email protected] Thomas Hofmann [email protected] Department of Computer Science, Brown University, Providence, RI 02912 Thorsten Joachims [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14853
https://www.aclweb.org/anthology/P19-1587/
In this paper, we propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. The semi-Markov property provides more accurate phrase-level predictions, thereby improving performance.Author: Chen-Tse Tsai, Ravneet Arora, Ketevan Tsereteli, Prabhanjan Kambadur, Yi Yang
https://www.quora.com/What-is-the-difference-between-regular-SVM-and-structural-SVM
Mar 13, 2014 · Structural SVM is a generalization of the SVM to allow structured output (e.g., trees). The standard equation computes a dot product between the learned weights and a feature mapping: [math]<w, \psi(x) y>[/math]. The difference in the structural...
http://www.cs.cornell.edu/people/tj/publications/joachims_etal_09b.pdf
ern machine learning methods like Boosting, Bagging, and Support Vector Machines (SVMs) (see e.g. [9]) have become the methods of choice for other problems in natural language processing (NLP) (e.g. word-sense disambiguation), parsing does not t into the conventional framework of classi cation and regression. In parsing, the prediction is not a ...
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