Optimization Techniques for Semi-Supervised Support Vector Machines

By Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi. Journal of machine learning research, 2008

455 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/480ddeb902c15861ef8f294f00c543dae508ee9b

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Citations in the corpus

Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization (2013, methodology): “The linear semi-supervised support vector machine problem can be formulated as follows [7]:”
Introduction to Semi-Supervised Learning (2009, background): “Since then, various nonconvex optimization techniques have been proposed to solve S3VMs [34], including semi-definite programming [55,54,188,189],gradient search with smooth approximation to the hat function [36], deterministic annealing [157], continuation method [32], concave-convex procedure (CCC”
Laplacian Support Vector Machines Trained in the Primal (2009, methodology): “For example, this intuition underlies the Transductive Support Vector Machines (Vapnik, 2000) and in its diﬀerent implementations, such as TSVM in (Joachims, 1999) or S 3 VM (Demiriz and Bennett, 2000; Chapelle et al., 2008).”
A survey on semi-supervised learning (2019, background): “However, the formulation in optimization problem 2 corresponds to the continuous approach; it underlies, for example, the concave-convex procedure, which decomposes the non-convex objective function into a convex and a concave component, and iteratively solves the optimization problem by replacing t”
$\propto$SVM for learning with label proportions (2013, methodology): “To alleviate this problem, similar to T-SVM (Joachims, 1999; Chapelle et al., 2008), the proposed alter-∝SVM algorithm (Algorithm 1) takes an additional annealing loop to gradually increase C.”
Machine Learning Methods for Attack Detection in the Smart Grid (2015, methodology): “Moreover, this requirement is satisfied in (19) by adjusting C2 [27].”
Fast and simple gradient-based optimization for semi-supervised support vector machines (2014, background): “[4, 22] and Zhu and Goldberg [5] for comprehensive surveys.”
Multi-Label Classification with Unlabeled Data: An Inductive Approach (2013, methodology): “In this paper, the ConCave Convex Procedure( CCCP ) method [Collobert et al. (2006)][Chapelle et al. (2008)] is applied to solve the non-convex optimization problem.”
Supervised classification and mathematical optimization (2013, methodology): “Several methods have been proposed to address this problem, as reviewed in [58], either exact or heuristic.”
Towards Making Unlabeled Data Never Hurt (2011, background): “Many empirical studies, however, show that there are cases in which the use of unlabeled data decreases the performance [7], [11], [13], [14], [16], [20], [36], [47], [50].”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, unknown): “Advanced works in S3V M are [54–56].”
Support Vector Machines with the Ramp Loss and the Hard Margin Loss (2011, background): “Bennet and Demiriz [3] and Chapelle [9] use branch-and-bound algorithms to derive globally optimal solutions to a semi-supervised support vector machine (S(3)VM).”
A Survey of Optimization Methods From a Machine Learning Perspective (2019, methodology): “There are various methods summarized in [79] to deal with this problem, such as the branch-and-bound techniques [80] and convex relaxation methods [81].”
Global optimization issues in deep network regression: an overview (2018, methodology): “We refer to [41] for a survey on semi-supervised methods which are not covered in this paper, to [36,53] and references therein for complete surveys on mathematical optimization for SVM, and to [107] for a survey on solution approaches for the convex training problem of different SVMs.”
Discriminative Semi-Supervised Feature Selection Via Manifold Regularization (2009, result): “This is also observed in [10], where different algorithms seem to have clearly different performance.”
Semi-Supervised and Unsupervised Extreme Learning Machines (2014, background): “Since SSL requires less effort to collect labeled data and can offer higher accuracy, it has been applied to various domains [41]–[43].”