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http://jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pdf
OPTIMIZATION TECHNIQUES FOR SEMI-SUPERVISED SUPPORT VECTOR MACHINES 3.1 Branch-and-Bound (BB) for Global Optimization The objective function (4) can be globally optimized using Branch-and-Bound techniques. This was
https://dl.acm.org/citation.cfm?id=1390688
Jun 01, 2008 · Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem.Cited by: 435
https://papers.nips.cc/paper/1582-semi-supervised-support-vector-machines.pdf
In this work we propose a method for semi-supervised support vector machines (S3VM). S3VM are constructed using a mixture of labeled data (the training set) and unlabeled data (the working set). The objective is to assign class labels to the working set such that the …
https://www.researchgate.net/publication/220320338_Optimization_Techniques_for_Semi-Supervised_Support_Vector_Machines
Optimization Techniques for Semi-Supervised Support Vector Machines. Article in Journal of Machine Learning Research 9(1):203-233 · February 2008 with 57 Reads How we measure 'reads'
https://www.sciencedirect.com/science/article/pii/S0925231213003706
The original problem formulation of semi-supervised support vector machines was given by Vapnik and Sterin under the name of transductive support vector machines. From an optimization point of view, the first approaches have been proposed in the late nineties by Joachims and Bennet and Demiriz .Cited by: 25
https://www.sciencedirect.com/science/article/pii/S0893608016300375
The semi-supervised support vector machine (S 3 VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S 3 VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over ...Cited by: 27
http://pages.cs.wisc.edu/~zhiting/nonlinear-report.pdf
result, Semi-Supervised Support Vector Machines(S3VMs) were developed. One difficulty is that the formulation is a non-convex optimization problem, and thus varieties optimization techniques were proposed for this problem. Each technique has its own advantages and disadvantages, and [2] does a survey of optimization techniques for S3VMs.
https://www.researchgate.net/publication/221345133_A_continuation_method_for_semi-supervised_SVMs
Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.4970
As a result, Semi-Supervised Support Vector Machines(S 3 VMs) were developed. One difficulty is that the formulation is a non-convex optimization problem, and thus varieties optimization techniques were proposed for this problem. Each technique has its own advantages and disadvantages, and [2] does a survey of optimization techniques for S 3 VMs.
https://webdocs.cs.ualberta.ca/~dale/papers/aaai05.pdf
Unsupervised and Semi-supervised Multi-class Support Vector Machines ... Efficient convex optimization techniques have had a pro-found impact on the field of machine learning. Most of their use to date, however, has been in applying quadratic pro-
http://jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pdf
OPTIMIZATION TECHNIQUES FOR SEMI-SUPERVISED SUPPORT VECTOR MACHINES 3.1 Branch-and-Bound (BB) for Global Optimization The objective function (4) can be globally optimized using Branch-and-Bound techniques. This was
https://dl.acm.org/citation.cfm?id=1390688
Jun 01, 2008 · Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem.Cited by: 435
https://www.researchgate.net/publication/220320338_Optimization_Techniques_for_Semi-Supervised_Support_Vector_Machines
Optimization Techniques for Semi-Supervised Support Vector Machines. Article in Journal of Machine Learning Research 9(1):203-233 · February 2008 with 57 Reads How we measure 'reads'
https://papers.nips.cc/paper/1582-semi-supervised-support-vector-machines.pdf
In this work we propose a method for semi-supervised support vector machines (S3VM). S3VM are constructed using a mixture of labeled data (the training set) and unlabeled data (the working set). The objective is to assign class labels to the working set such that the …
https://www.sciencedirect.com/science/article/pii/S0893608016300375
The semi-supervised support vector machine (S 3 VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S 3 VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over ...Cited by: 27
https://www.sciencedirect.com/science/article/pii/S0925231213003706
The original problem formulation of semi-supervised support vector machines was given by Vapnik and Sterin under the name of transductive support vector machines. From an optimization point of view, the first approaches have been proposed in the late nineties by Joachims and Bennet and Demiriz .Cited by: 25
http://www.cs.cmu.edu/%7Eninamf/courses/601sp15/slides/18_svm-ssl_03-25-2015.pdf
• Support Vector Machines (SVMs). • Semi-Supervised SVMs. • Semi-Supervised Learning. ... • Famous example of constrained optimization: linear programming, where objective fn is linear, constraints are linear (in)equalities ... Support Vector Machines (SVMs)
http://pages.cs.wisc.edu/~zhiting/nonlinear-report.pdf
result, Semi-Supervised Support Vector Machines(S3VMs) were developed. One difficulty is that the formulation is a non-convex optimization problem, and thus varieties optimization techniques were proposed for this problem. Each technique has its own advantages and disadvantages, and [2] does a survey of optimization techniques for S3VMs.
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.4970
As a result, Semi-Supervised Support Vector Machines(S 3 VMs) were developed. One difficulty is that the formulation is a non-convex optimization problem, and thus varieties optimization techniques were proposed for this problem. Each technique has its own advantages and disadvantages, and [2] does a survey of optimization techniques for S 3 VMs.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.125.7035
Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S 3 VMs. This paper reviews key ideas in this literature.
https://link.springer.com/chapter/10.1007/978-0-85729-504-0_4
Efficient convex optimization techniques have had a profound impact on the field of machine learning, such as quadratic programming and linear programming techniques to Support Vector Machine and other kernel machine training.
http://www.fabiangieseke.de/pdfs/icpram2012.pdf
SPARSE QUASI-NEWTON OPTIMIZATION FOR SEMI-SUPERVISED SUPPORT VECTOR MACHINES Fabian Gieseke 1, Antti Airola 2, Tapio Pahikkala , and Oliver Kramer 1Computer Science Department, Carl von Ossietzky Universitat Oldenburg, 26111 Oldenburg, Germany¨ 2Turku Centre for Computer Science, Department of Information Technology, University of Turku, 20520 Turku, Finland
https://link.springer.com/article/10.1007/s10957-015-0843-4
Nov 16, 2015 · Semi-supervised support vector machines arise in machine learning as a model of mixed integer programming problem for classification. In this paper, we propose two convex conic relaxations for the original mixed integer programming problem. The first one is a new semi-definite relaxation, and its possibly maximal ratio of the optimal value is estimated approximately. The second …
https://arxiv.org/abs/1610.03995
Oct 13, 2016 · In addition, many existing AL techniques pay too little attention to their practical applicability. To meet these challenges, this article presents several techniques that together build a new approach for combining AL and semi-supervised learning (SSL) for support vector machines (SVM) in classification tasks.
http://www.ntu.edu.sg/home/ctguan/Publications/C_2007_Yuanqing_IEEE_ICASSP.pdf
A SELF-TRAINING SEMI-SUPERVISED SUPPORT VECTOR MACHINE ALGORITHM AND ITS APPLICATIONS IN BRAIN COMPUTER INTERFACE Yuanqing Li, Huiqi Li, Cuntai Guan and Zhengyang Chin Institute for Infocomm Research, Singapore 119613 ABSTRACT In this paper, we analyze the convergence of an iterative self-training semi-supervised support vector machine …
https://dl.acm.org/citation.cfm?id=340671
Hoai Minh Le , Hoai An Le Thi , Manh Cuong Nguyen, DCA based algorithms for feature selection in semi-supervised support vector machines, Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition, p.528-542, …
https://www.scirp.org/journal/PaperInformation.aspx?paperID=28185
Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Smoothing homotopy method is one of feasible method for solving semi-supervised support vector machines. In this paper, an inexact implementation of the smoothing homotopy method is considered. The numerical implementation is based on a truncated smoothing technique.
http://auai.org/uai2016/proceedings/papers/110.pdf
termed as Budgeted Semi-supervised Support Vector Ma-chine (BS3VM). To devise BS3VM, we first conjoin the theory of kernel method with the framework of spectral-graph-based semi-supervised learning. This allows us to form a specific optimization problem which involves the core optimization problem of kernel method and simulta-
http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf
example: semi-supervised learning requires combinatorial / nonconvex / global optimization techniques. Several current topics in optimization may be applicable to machine learning problems. Stephen Wright (UW-Madison) Optimization in SVM Comp Learning Workshop 3 / 56 ... Optimization Algorithms in Support Vector Machines
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category ...
https://core.ac.uk/display/20900168
Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S 3 VMs. This paper reviews key ideas in this literature.
http://papers.nips.cc/paper/3135-branch-and-bound-for-semi-supervised-support-vector-machines.pdf
Semi-supervised SVMs (S3VM) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The asso-ciated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally ...
http://www.cst.ecnu.edu.cn/~slsun/pubs/ROMSVM.pdf
Support vector machines (SVMs) are theoretically well-justified machine learning techniques, which have also been successfully applied to many real-world domains. The use of optimization methodologies plays a central role in finding solutions of SVMs. This paper reviews representative and state-of-the-art techniques for opti-
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