Scalable kernel systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2130, Issue , 2001, Pages 285-291

  Tresp, Volker ^a   Schwaighofer, Anton ^a,b  

^a SIEMENS AG   (Germany)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

APPROXIMATE METHODS; COMPUTATIONAL LOADS; LARGE DATASETS; SCALING KERNEL; TRAINING DATA;

NEURAL NETWORKS;

EID: 70450198857     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_41     Document Type: Conference Paper

References (6)

1. Lee, Y.-J. and Mangasarian, O. L.: RSVM: Reduced Support Vector Machines. Data Mining Institute Technical Report 00-07, Computer Sciences Department, University of Wisconsin (2000)
2. Smola, A. J. and Bartlett, P.: Sparse Greedy Gaussian Process Regression. In: T. K. Leen, T. G. Diettrich and V. Tresp, (eds.): Advances in Neural Information Processing Systems 13 (2001)
3. Tresp, V.: The Bayesian Committee Machine. Neural Computation, Vol.12 (2000)
4. Tresp, V.: Scaling Kernel-Based Systems to Large Data Sets. Data Mining and Knowledge Discovery, accepted for publication
5. Wahba, G.: Spline models for observational data. Philadelphia: Society for Industrial and Applied Mathematics (1990)
6. Williams, C. K. I. and Seeger, M.: Using the Nyström Method to Speed up Kernel Machines. In: T. K. Leen, T. G. Diettrich and V. Tresp, (eds.): Advances in Neural Information Processing Systems 13 (2001)