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https://link.springer.com/chapter/10.1007%2F978-3-540-87732-5_56
In this paper, still following the above line of the research, we develop a novel algorithm, termed as Structural Support Vector Machine (SSVM), by directly embedding the structural information into the SVM objective function rather than using as the constraints into SLMM, in this way, we achieve: 1) to overcome the above three shortcomings; 2 ...Cited by: 22
https://www.tandfonline.com/doi/abs/10.3846/13923730.2015.1005021
Sep 04, 2007 · This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems.Cited by: 14
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://theprofessionalspoint.blogspot.com/2019/03/advantages-and-disadvantages-of-svm.html
Mar 01, 2019 · Advantages and Disadvantages of SVM (Support Vector Machine) in Machine Learning ... SVM tries to find the best and optimal hyperplane which has maximum margin from each Support Vector. Kernel functions / tricks are used to classify the non-linear data. It transforms non-linear data into linear data and then draws a hyperplane.Location: Gurgaon, India
http://ix.cs.uoregon.edu/~lowd/icml14torkamani.pdf
On Robustness and Regularization of Structural Support Vector Machines 2013, based on the political blogs dataset from Adamic and Glance (2005). Blogs are classified as liberal or conserva-tive using both their words and link structure. To make this more challenging, we train on blogs from 2004 but evalu-ate on every year, from 2003 to 2013.
http://www.cs.cornell.edu/people/tj/svm_light/svm_hmm.html
Support Vector Machine Learning for Interdependent and Structured Output Spaces. International Conference on Machine Learning (ICML), 2004. [Postscript] [PDF] Y. Altun, I. Tsochantaridis, T. Hofmann, Hidden Markov Support Vector Machines. International Conference on Machine …
https://www.youtube.com/watch?v=1NxnPkZM9bc
Jan 06, 2014 · In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at https: ...Author: Thales Sehn Körting
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...
https://en.wikipedia.org/wiki/Structural_risk_minimization
Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data ...
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