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edited Jun 12 at 10:30

Seminal NIPS paper:

Schölkopf, B. (2000). The kernel trick for distances. In Neural Information Processing Systems, pages 301-307.

There are many kernel design papers and books:

Schölkopf, B. and Smola, A. J. (2002). Learning with kernels : support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press.
Pekalska, E., Paclik, P., and Duin, R. P. W. (2002). A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res., 2:175-211.
Pekalska, E. and Duin, R. P. W. (2005). The Dissimilarity Representation for Pattern Recognition. World Scientific Publishing Co., Inc., River Edge, NJ, USA.
Haasdonk, B. and Bahlmann, C. (2004). Learning with distance substitution kernels. Pattern Recognition, volume 3175 of Lecture Notes in Computer Science, pages 220-227-227.

Seminal NIPS paper:

Schölkopf, B. (2000). The kernel trick for distances. In Neural Information Processing Systems, pages 301-307.

There are many kernel design papers and books:

Schölkopf, B. and Smola, A. J. (2002). Learning with kernels : support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press.
Pekalska, E., Paclik, P., and Duin, R. P. W. (2002). A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res., 2:175-211.
Pekalska, E. and Duin, R. P. W. (2005). The Dissimilarity Representation for Pattern Recognition. World Scientific Publishing Co., Inc., River Edge, NJ, USA.
Haasdonk, B. and Bahlmann, C. (2004). Learning with distance substitution kernels. Pattern Recognition, volume 3175 of Lecture Notes in Computer Science, pages 220-227-227.

Seminal NIPS paper:

Schölkopf, B. (2000). The kernel trick for distances. In Neural Information Processing Systems, pages 301-307.

There are many kernel design papers and books:

Schölkopf, B. and Smola, A. J. (2002). Learning with kernels : support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press.
Pekalska, E., Paclik, P., and Duin, R. P. W. (2002). A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res., 2:175-211.
Pekalska, E. and Duin, R. P. W. (2005). The Dissimilarity Representation for Pattern Recognition. World Scientific Publishing Co., Inc., River Edge, NJ, USA.
Haasdonk, B. and Bahlmann, C. (2004). Learning with distance substitution kernels. Pattern Recognition, volume 3175 of Lecture Notes in Computer Science, pages 220-227.

Source Link

created Apr 14, 2015 at 14:39

Seminal NIPS paper:

Schölkopf, B. (2000). The kernel trick for distances. In Neural Information Processing Systems, pages 301-307.

There are many kernel design papers and books:

Schölkopf, B. and Smola, A. J. (2002). Learning with kernels : support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press.
Pekalska, E., Paclik, P., and Duin, R. P. W. (2002). A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res., 2:175-211.
Pekalska, E. and Duin, R. P. W. (2005). The Dissimilarity Representation for Pattern Recognition. World Scientific Publishing Co., Inc., River Edge, NJ, USA.
Haasdonk, B. and Bahlmann, C. (2004). Learning with distance substitution kernels. Pattern Recognition, volume 3175 of Lecture Notes in Computer Science, pages 220-227-227.