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https://isn.ucsd.edu/pub/papers/nips00_inc.pdf
versible, and decremental “unlearning” offers an efficient method to ex-actly evaluate leave-one-outgeneralization performance. Interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data. 1 Introduction Training a support vector machine (SVM) requires solving a ...
http://papers.nips.cc/paper/1814-incremental-and-decremental-support-vector-machine-learning.pdf
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn Tucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is re versible, and decremental "unlearning" offers an efficient method to ex
https://www.semanticscholar.org/paper/Incremental-and-Decremental-Support-Vector-Machine-Cauwenberghs-Poggio/e3948c28d605e0d90e88e160556cfc14fbba57c8
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn-Tucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decremental "unlearning" offers an efficient method to exactly evaluate leave-one-out generalization ...
https://www.researchgate.net/publication/2373982_Incremental_and_Decremental_Support_Vector_Machine_Learning
Incremental and Decremental Support Vector Machine Learning. ... Interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of ...
https://dl.acm.org/citation.cfm?id=3008808
Incremental and decremental support vector machine learning. Pages 388–394. Previous Chapter Next Chapter. ABSTRACT. An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn-Tucker conditions on all previously seen training data, in a number of steps each ...Cited by: 1444
http://isn.ucsd.edu/svm/incremental/
Incremental and Decremental Support Vector Machine Learning Matlab code, and examples ... svcm_*.m support vector classification machine kernel.m kernel function used in svcm_*.m test*.m example demo scripts Set*.m graphics formatting gen*.m data generating functions Matlab sample data: *.mat vectors x [L,N ...
https://link.springer.com/chapter/10.1007%2F978-3-540-74690-4_22
We present a method to find the exact maximal margin hyperplane for linear Support Vector Machines when a new (existing) component is added (removed) to (from) the inner product. The maximal margin hyperplane with the new inner product is obtained in terms of that for the old inner product, without re-computing it from scratch and the procedure ...Cited by: 6
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