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https://isn.ucsd.edu/pub/papers/nips00_inc.pdf
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 quadratic programming (QP) problem in a number of coefficients equal to the number of training examples. For very
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://isn.ucsd.edu/svm/incremental/
Incremental and Decremental Support Vector Machine Learning Matlab code, and examples Gert Cauwenberghs. Content. This site provides freely downloadable Matlab code, data files, and example scripts for incremental SVM classification, including exact leave-one-out (LOO) cross-validation.
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/312532410_Incremental_and_decremental_support_vector_machine_learning
The least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, maneuvering tests are carried out based...
https://www.researchgate.net/publication/2373982_Incremental_and_Decremental_Support_Vector_Machine_Learning
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously seen training data,...
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://papers.nips.cc/paper/3804-multiple-incremental-decremental-learning-of-support-vector-machines.pdf
We propose a multiple incremental decremental algorithm of Support Vector Ma-chine (SVM). Conventional single incremental decremental SVM can update the trained model efficiently when single data point is added to or removed from the training set. When we add and/or remove multiple data points, this algorithm is time-consuming because we need to repeatedly apply it to each data point. The
https://link.springer.com/article/10.1007/s10586-018-1772-4
Jan 17, 2018 · In view of the long execution time and low execution efficiency of Support Vector Machine in large-scale training samples, the paper has proposed the online incremental and decremental learning algorithm based on variable support vector machine (VSVM).Cited by: 14
https://dl.acm.org/doi/10.1145/2623330.2623661
M. Karasuyama and I. Takeuchi. Multiple incremental decremental learning of support vector machines. IEEE TNN, 21:1048--1059, 2010. Google Scholar Digital Library; G. S. Kimeldorf and G. Wahba. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Ann. Math. Stat., 41:495--502, 1970. Google Scholar Cross Ref
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