Kernel-Machines.Org software links Kernel-Machines.Org Frontpage: This page is devoted to learning methods building on kernels, such as the support vector machine. It grew out of earlier pages at the Max Planck Institute for Biological Cybernetics and at GMD FIRST, snapshots of which can be found here and here. In those days, information about kernel methods was sparse and nontrivial to find, and the kernel machines web site acted as a central repository for the field. It included a list of people working in the field, and online preprints of most publications. Nowadays, this no longer makes sense, partly because the field is very popular, so there are too many people and papers to make such lists useful, and partly because search engines do the job much more conveniently. But what really forced us to do a major update of the site was the fact that spammers discovered our site, and it was no longer possible to operate a system which was built on the trust that people who submit an entry do so to improve the quality of the site.

References in zbMATH (referenced in 23 articles )

Showing results 1 to 20 of 23.
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  1. Bietti, Alberto; Mairal, Julien: Group invariance, stability to deformations, and complexity of deep convolutional representations (2019)
  2. Burl, Michael C.; Wetzler, Philipp G.: Onboard object recognition for planetary exploration (2011) ioport
  3. Chmielnicki, Wiesław; Stąpor, Katarzyna: Investigation of normalization techniques and their impact on a recognition rate in handwritten numeral recognition (2010) ioport
  4. Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Changiz, Sahar: Emotional stress recognition system for affective computing based on bio-signals (2010)
  5. Kim, Sang-Ki; Park, Youn Jung; Toh, Kar-Ann; Lee, Sangyoun: SVM-based feature extraction for face recognition (2010)
  6. Zhang, Changshui; Wang, Fei: A multilevel approach for learning from labeled and unlabeled data on graphs (2010)
  7. Maglogiannis, Ilias; Zafiropoulos, Elias; Anagnostopoulos, Ioannis: An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers (2009) ioport
  8. Mucherino, A.; Papajorgji, Petraq; Pardalos, P. M.: A survey of data mining techniques applied to agriculture (2009)
  9. Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J.: Kernel methods in machine learning (2008)
  10. Smola, Alex; Gretton, Arthur; Song, Le; Schölkopf, Bernhard: A Hilbert space embedding for distributions (2007)
  11. Vishwanathan, S. V. N.; Smola, Alexander J.; Vidal, René: Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes (2007)
  12. Li, Zhanchun; Li, Zhitang; Liu, Bin: Anomaly detection system based on principal component analysis and support vector machine (2006)
  13. Mangasarian, O. L.; Rosen, J. B.; Thompson, Michael E.: Convex kernel underestimation of functions with multiple local minima (2006)
  14. Chen, Songcan; Li, Daohong: Modified linear discriminant analysis (2005)
  15. Fung, Glenn M.; Mangasarian, O. L.: Multicategory proximal support vector machine classifiers (2005)
  16. Fung, Glenn M.; Mangasarian, O. L.: Multicategory proximal support vector machine classifiers (2005)
  17. Serafini, Thomas; Zanghirati, Gaetano; Zanni, Luca: Gradient projection methods for quadratic programs and applications in training support vector machines (2005)
  18. Chen, Songcan; Yang, Xubing: Alternative linear discriminant classifier (2004)
  19. Yang, Jian; Jin, Zhong; Yang, Jing-yu; Zhang, David; Frangi, Alejandro F.: Essence of kernel Fisher discriminant: KPCA plus LDA (2004) ioport
  20. Schölkopf, Bernhard; Smola, Alexander J.: A short introduction to learning with kernels (2003)

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Further publications can be found at: