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https://isn.ucsd.edu/pub/papers/nips00_inc.pdf
Incremental and Decremental Support Vector Machine Learning Gert Cauwenberghs CLSP, ECE Dept. Johns Hopkins University Baltimore, MD 21218 [email protected] Tomaso Poggio CBCL, BCS Dept. Massachusetts Institute of Technology Cambridge, MA 02142 [email protected] Abstract An on-linerecursive algorithm for training support vector machines, one
https://www.researchgate.net/publication/2373982_Incremental_and_Decremental_Support_Vector_Machine_Learning
An adiabatic incremental support vector machine (SVM) learning paradigm was introduced in [4]. A method known as bookkeeping was proposed to compute the new coefficients of the SVM model. ...
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 ...
https://www.academia.edu/14378308/Incremental_and_Decremental_Support_Vector_Machine_Learning
Incremental and Decremental Support Vector Machine Learning Gert Cauwenberghs Tomaso Poggio CLSP, ECE Dept. CBCL, BCS Dept. Johns Hopkins University Massachusetts Institute of Technology Baltimore, MD 21218 Cambridge, MA 02142 [email protected] [email protected] Abstract An on-line recursive algorithm for training support vector machines, one vector at a time, is presented.
https://www.researchgate.net/publication/312532410_Incremental_and_decremental_support_vector_machine_learning
In this paper we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to …
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 ...
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