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https://en.wikipedia.org/wiki/Transduction_(machine_learning)
An example of an algorithm in this category is the Transductive Support Vector Machine (TSVM). A third possible motivation which leads to transduction arises through the need to approximate. If exact inference is computationally prohibitive, one may at least try to make sure that …
http://www.cs.cmu.edu/~guestrin/Class/10701-S06/Slides/tsvms-pca.pdf
Transductive SVMs [Joachims 99] w. x + b = + 1 w. x + b = - 1 w. x + b = 0 m a r g i n γ If you set to zero →ignore unlabeled data Intuition of algorithm: start with small add labels to some unlabeled data based on classifier prediction slowly increase keep on labeling unlabeled data and re-running classifier
https://calculatedcontent.com/2014/09/23/machine-learning-with-missing-labels-transductive-svms/
Sep 23, 2014 · Machine Learning with Missing Labels: Transductive SVMs. September 23, 2014 Charles H Martin, PhD Uncategorized 15 comments. SVMs are great for building text classifiers–if you have a set of very high quality, labeled documents. ... W. Pan On Transductive Support Vector Machines [9] ...
http://users.stat.umn.edu/~xshen/paper/tsvm.pdf
transductive support vector machine (TSVM; Vapnik, 1998), which remains mysterious, particularly its “al-leged” unstable performance in empirical studies. TSVM seeks the largest separation between labeled and unlabeled data through regularization. In em-pirical studies, it performs well in text classification
https://www.sciencedirect.com/science/article/pii/S0167865503000084
Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead of an inductive one in support vector classifiers, the working set can be used as …Cited by: 188
https://www.sciencedirect.com/science/article/pii/S2212671612000601
This article is a further study on transductive learning, trying to find a more common transductive learning algorithm than the existing methods. According to the inherent characteristics of support vector machine classification, this article design a transductive support vector machine algorithm based on spectral clustering (Shi and Malik, 2000).Cited by: 2
https://www.quora.com/What-are-the-Transductive-Support-Vector-Machines-TSVMs
The objective function for regular SVM maximizes the margin, alongwith the constraints that positive datapoints and negative datapoints are on opposite sides of the separating hyperplane. The regular SVM formulation can use only labeled datapoints...
https://www.cs.cornell.edu/people/tj/svm_light/
Training algorithm for transductive Support Vector Machines. Integrated core QP-solver based on the method of Hildreth and D'Espo. Uses folding in the linear case, which speeds up linear SVM training by an order of magnitude. Allows linear cost models. Faster in general. V2.00 - V2.01. Improved interface to PR_LOQO Source code for SVM light V2.01
https://en.wikipedia.org/wiki/Support-vector_machine
Transductive support-vector machines. Transductive support-vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning by following the principles of transduction. Here, in addition to the training set , the learner is also given a set
https://www.cs.cornell.edu/people/tj/svm_light/old/svm_light_v4.00.html
SVM light is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following: ... The algorithm has scalable memory requirements and can handle problems with many thousands of support vectors efficiently. ... Training algorithm for …
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