LIBSVM

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail: http://dl.acm.org/citation.cfm?id=1961199


References in zbMATH (referenced in 1114 articles )

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  1. Lu, Haihao; Freund, Robert M.: Generalized stochastic Frank-Wolfe algorithm with stochastic “substitute” gradient for structured convex optimization (2021)
  2. Mudunuru, M. K.; Karra, S.: Physics-informed machine learning models for predicting the progress of reactive-mixing (2021)
  3. Rodomanov, Anton; Nesterov, Yurii: Greedy quasi-Newton methods with explicit superlinear convergence (2021)
  4. Sun, Ruoyu; Ye, Yinyu: Worst-case complexity of cyclic coordinate descent: (O(n^2)) gap with randomized version (2021)
  5. Uribe, César A.; Lee, Soomin; Gasnikov, Alexander; Nedić, Angelia: A dual approach for optimal algorithms in distributed optimization over networks (2021)
  6. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)
  7. Bauermeister, Christoph; Keren, Hanna; Braun, Jochen: Unstructured network topology begets order-based representation by privileged neurons (2020)
  8. Bellavia, Stefania; Krejić, Nataša; Morini, Benedetta: Inexact restoration with subsampled trust-region methods for finite-sum minimization (2020)
  9. Blanco, Victor; Puerto, Justo; Rodriguez-Chia, Antonio M.: On (\ell_p)-support vector machines and multidimensional kernels (2020)
  10. Chan, Raymond H.; Kan, Kelvin K.; Nikolova, Mila; Plemmons, Robert J.: A two-stage method for spectral-spatial classification of hyperspectral images (2020)
  11. Chelly Dagdia, Zaineb; Elouedi, Zied: A hybrid fuzzy maintained classification method based on dendritic cells (2020)
  12. Cimen, E.; Ozturk, G.: O-PCF algorithm for one-class classification (2020)
  13. Colombo, Tommaso; Sagratella, Simone: Distributed algorithms for convex problems with linear coupling constraints (2020)
  14. Daoudi, Mohamed; Alvarez Paiva, Juan-Carlos; Kacem, Anis: The Riemannian and affine geometry of facial expression and action recognition (2020)
  15. Elman, Miriam R.; Minnier, Jessica; Chang, Xiaohui; Choi, Dongseok: Noise accumulation in high dimensional classification and total signal index (2020)
  16. Fercoq, Olivier; Qu, Zheng: Restarting the accelerated coordinate descent method with a rough strong convexity estimate (2020)
  17. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  18. García-Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Alonso Fernández, J. R.; Díaz Muñiz, C.: A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study (2020)
  19. Gu, Bin; Xian, Wenhan; Huo, Zhouyuan; Deng, Cheng; Huang, Heng: A unified (q)-memorization framework for asynchronous stochastic optimization (2020)
  20. Gustavo Henrique de Rosa, João Paulo Papa, Alexandre Xavier Falcão: OPFython: A Python-Inspired Optimum-Path Forest Classifier (2020) arXiv

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