On transductive support vector machines

Shen, Xiaotong; Wang, J; Pan, Wei

doi:10.1090/conm/443/08551

Cited by 58 publications

(29 citation statements)

References 15 publications

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“…Indeed, it refers to methods that use a large unlabelled dataset, together with a small labelled dataset during the training stage (Wang, 2007). Liu et al (2016) compare between 3 distinct semi-supervised learning techniques, i.e.…”

Section: Classification Methodsmentioning

confidence: 99%

“…The system uses several SVMs for classifying documents into a relevant and irrelevant class. In addition to the ordinary SVM, they also employed the transductive support vector machines (TSVM; Wang, 2007) and tri-class support vector machines (3C-SVM) introduced by Yang et al (2015). The latter semi-supervised algorithms were found to outperform the supervised naïve Bayes classifier.…”

Section: Classification Methodsmentioning

confidence: 99%

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Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Jaspers

Troyer

Aerts

2018

EFS3

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This Report presents the results from EFSA project RC/EFSA/AMU/2016/01 related to the implementation of machine learning techniques for literature reviews and systematic reviews in EFSA. An overview of the different steps of a systematic review is provided, along with possible ways for automation. Although it was found that most steps could benefit from automation, it was also observed that some steps require more sophisticated methods than those encompassed within the machine learning framework. Availability of data and methodology allowed for the development of an automatic screening tool based on several machine learning techniques. The developed shiny R application can be used for the screening of abstracts and full texts. Properties of machine learning techniques are discussed in this Report together with their most important advantages and disadvantages. The latter discussion includes both general properties, as well as context-specific properties based on their performance in three case studies. Although creating a universal automatic data extraction tool was considered to be infeasible in this stage, this step of the systematic review was addressed to allow the reviewer to scan the uploaded pdf files for certain words or string of words. Based on observations from the performed case studies, recommendations were made regarding which methods are preferred in specific situations. More explicitly, a discussion is made about the performance of the classifiers with respect to the magnitude of the pool of papers to be screened as well as to the amount of imbalance, referring to the proportion of relevant and irrelevant papers. Finally, it was concluded that the results presented in this report provide proof that the developed shiny application could be efficiently used in combination with other software such as DistillerSR. © European Food Safety Authority, 2018Key words: Systematic Reviews, Machine Learning, screening, data extraction, Sensitivity, Specificity Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Reproduction is authorised provided the source is acknowledged. Machine Learning Techniques for Literature and Systematic Reviewswww.efsa.europa.eu/publications 3 EFSA Supporting publication 2018:EN-1427The present document has been produced and adopted by the bodies identified above as author. This task has been carried out exclusively by the author in the context of a contract ...

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Section: Classification Methodsmentioning

confidence: 99%

Section: Classification Methodsmentioning

confidence: 99%

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Jaspers

Troyer

Aerts

2018

EFS3

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“…Problem (5) is the TSVM formulation, which can be solved efficiently by one of the methods described in [12], [33], [35], [10].…”

Section: B Formulationmentioning

confidence: 99%

Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning

Bahadori

Liu

Zhang

2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only unlabeled examples are available in the target domain. It easily finds applications in spam filtering, microblogging mining and so on. In this paper, we propose a general framework to solve the problem by mapping the input features in both the source domain and target domain into a shared latent space and simultaneously minimizing the feature reconstruction loss and prediction loss. We develop one specific example of the framework, namely latent large-margin transductive transfer learning (LATTL) algorithm, and analyze its theoretic bound of classification loss via Rademacher complexity. We also provide a unified view of several popular transfer learning algorithms under our framework. Experiment results on one synthetic dataset and three application datasets demonstrate the advantages of the proposed algorithm over the other stateof-the-art ones.

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“…In order to build a classifier with the best possible generalization performance, we have defined a new partition which differs from the one traditionally proposed [27,30]. The procedure is described below.…”

Section: Semi-supervised Classificationmentioning

confidence: 99%

Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers

Oussar

Ahriz

Denby

et al. 2011

J Wireless Com Network

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A new approach to indoor localization is presented, based upon the use of Received Signal Strength (RSS) fingerprints containing data from very large numbers of cellular base stations-up to the entire GSM band of over 500 channels. Machine learning techniques are employed to extract good quality location information from these high-dimensionality input vectors. Experimental results in a domestic and an office setting are presented, in which data were accumulated over a 1-month period in order to assure time robustness. Room-level classification efficiencies approaching 100% were obtained, using Support Vector Machines in one-versus-one and one-versus-all configurations. Promising results using semi-supervised learning techniques, in which only a fraction of the training data is required to have a room label, are also presented. While indoor RSS localization using WiFi, as well as some rather mediocre results with low-carrier count GSM fingerprints, have been discussed elsewhere, this is to our knowledge the first study to demonstrate that good quality indoor localization information can be obtained, in diverse settings, by applying a machine learning strategy to RSS vectors that contain the entire GSM band.

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On transductive support vector machines

Cited by 58 publications

References 15 publications

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning

Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers

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