Description (homepage): SVMlight is an implementation of Vapnik’s Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. The optimization algorithms used in SVMlight are described in [Joachims, 2002a ]. [Joachims, 1999a]. The algorithm has scalable memory requirements and can handle problems with many thousands of support vectors efficiently. The software also provides methods for assessing the generalization performance efficiently. It includes two efficient estimation methods for both error rate and precision/recall. XiAlpha-estimates [Joachims, 2002a, Joachims, 2000b] can be computed at essentially no computational expense, but they are conservatively biased. Almost unbiased estimates provides leave-one-out testing. SVMlight exploits that the results of most leave-one-outs (often more than 99%) are predetermined and need not be computed [Joachims, 2002a]. New in this version is an algorithm for learning ranking functions [Joachims, 2002c]. The goal is to learn a function from preference examples, so that it orders a new set of objects as accurately as possible. Such ranking problems naturally occur in applications like search engines and recommender systems. Futhermore, this version includes an algorithm for training large-scale transductive SVMs. The algorithm proceeds by solving a sequence of optimization problems lower-bounding the solution using a form of local search. A detailed description of the algorithm can be found in [Joachims, 1999c]. A similar transductive learner, which can be thought of as a transductive version of k-Nearest Neighbor is the Spectral Graph Transducer. SVMlight can also train SVMs with cost models (see [Morik et al., 1999]). The code has been used on a large range of problems, including text classification [Joachims, 1999c][Joachims, 1998a], image recognition tasks, bioinformatics and medical applications. Many tasks have the property of sparse instance vectors. This implementation makes use of this property which leads to a very compact and efficient representation.

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  1. Ding, Xiaojian; Jin, Sheng; Lei, Ming; Yang, Fan: A predictor-corrector affine scaling method to train optimized extreme learning machine (2022)
  2. Blanchard, Gilles; Deshmukh, Aniket Anand; Dogan, Urun; Lee, Gyemin; Scott, Clayton: Domain generalization by marginal transfer learning (2021)
  3. Loor, Marcelo; De Tré, Guy: Handling subjective information through augmented (fuzzy) computation (2020)
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  10. Bacciu, Davide; Carta, Antonio; Gnesi, Stefania; Semini, Laura: An experience in using machine learning for short-term predictions in smart transportation systems (2017)
  11. Duarte Silva, A. Pedro: Optimization approaches to supervised classification (2017)
  12. Farooq, Muhammad; Steinwart, Ingo: An SVM-like approach for expectile regression (2017)
  13. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  14. Bai, Yan-Qin; Shen, Kai-Ji: Alternating direction method of multipliers for (\ell_1)-(\ell_2)-regularized logistic regression model (2016)
  15. Bloom, Veronica; Griva, Igor; Quijada, Fabio: Fast projected gradient method for support vector machines (2016)
  16. Doğan, Ürün; Glasmachers, Tobias; Igel, Christian: A unified view on multi-class support vector classification (2016)
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  18. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  19. Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen: Efficient learning of supervised kernels with a graph-based loss function (2016)
  20. Zheng, Songfeng: Smoothly approximated support vector domain description (2016)

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