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https://people.eecs.berkeley.edu/~malik/cs294/decoste-scholkopf.pdf
TRAINING INVARIANT SUPPORT VECTOR MACHINES 163 One way to look at feature selection is that it changes the representation of the data, and in this, it is not so different from another method for incorporating prior knowledge
https://link.springer.com/article/10.1023/A:1012454411458
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training...Cited by: 695
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://www.researchgate.net/publication/2924845_Training_Invariant_Support_Vector_Machines
Training Invariant Support Vector Machines. ... support vector machines, ... training methods scale at least quadratically in the number of training examples. Thus, the
https://dl.acm.org/citation.cfm?id=599613.599672
The article presents a general view of a class of decomposition algorithms for training Support Vector Machines (SVM) which are motivated by the method of feasible directions. The first such algorithm for the pattern recognition SVM has been proposed ...Cited by: 695
https://www.academia.edu/6781547/Training_Invariant_Support_Vector_Machines
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods
http://leon.bottou.org/publications/pdf/loosli-2006.pdf
Invariant SVM using Selective Sampling Training Invariant Support Vector Machines using Selective Sampling Ga elle Loosli [email protected] St ephane Canu [email protected] LITIS, INSA de Rouen Saint Etienne du Rouvray, 76801, France L eon Bottou [email protected] NEC laboratories of America Princeton, NJ 08540, USA Abstract
https://www.academia.edu/6781549/Training_Invariant_Support_Vector_Machines
Training Invariant Support Vector Machines
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.1038
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental ...
https://scikit-learn.org/stable/modules/svm.html
Support Vector Machines are powerful tools, but their compute and storage requirements increase rapidly with the number of training vectors. ... Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it ...
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