Incremental and Decremental Support Vector Machine Learning

By Gert Cauwenberghs, Tomaso A. Poggio. 2000

1,424 citations recorded by the source corpus.

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In Statistical Learning Theory

Citations in the corpus

Poisoning Attacks against Support Vector Machines (2012, methodology): “In this algorithm, the attack vector x c , y c } using incremental SVM ( e.g. , Cauwenberghs & Poggio, 2001).”
Model-Reuse Attacks on Deep Learning Systems (2018, background): “One may suggest using incremental learning [15, 48], which retrains an existing model to accommodate new data, or open-set learning, which extends a given model to new classes during inference [4, 51].”
Is Feature Selection Secure against Training Data Poisoning? (2015, background): “To overcome these issues, as in Cauwenberghs & Poggio (2000); Biggio et al. (2012), we assume that the Karush-Kuhn-Tucker (KKT) conditions under perturbation of the attack point x c remain satisﬁed, i.e. , we adjust the solution to remain at the optimum.”
Security Evaluation of Support Vector Machines in Adversarial Environments (2014, methodology): “After each update of the attack point xp, the optimal solution can be efficiently recomputed from the solution on Dtr, using the incremental SVM machinery [20].”
Support vector machines under adversarial label contamination (2015, methodology): “This however includes the development of suitable bookkeeping conditions, similarly to [38, 39], and it is thus left to future investigation.”
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019, methodology): “Speciﬁ-cally, an on-line recursive algorithm for SVM has been studied to facilitate learning one vector at a time (Cauwenberghs and Poggio 2001) and a local incremental approach has been proposed (Ralaivola and dAlch´e Buc 2001) to learn a SVM based on Radial Basis Function Kernel.”
DeltaGrad: Rapid retraining of machine learning models (2020, background): “Updates have been proposed for support vector machines (Syed et al., 1999; Cauwenberghs & Poggio, 2001) and nearest neighbors (Schelter, 2019).”
Memory-Efficient Incremental Learning Through Feature Adaptation (2020, neutral): “The literature for incremental learning prior to the deep-learning era includes incrementally trained support vector machines [5], random forests [32], and metricbased methods that generalize to new classes [28].”
Incremental Learning in Online Scenario (2020, methodology): “An alternative method is proposed in [3] by retaining the Karush-Kuhn-Tucker conditions instead of support vectors on old data and then update the solution using new data.”
Knowledge Distillation for Incremental Learning in Semantic Segmentation (2019, background): “Catastrophic forgetting has been faced even before the rise of neural networks popularity [26, 27, 28] and more recently has been rediscovered and tackled in different ways.”
Incremental Learning Techniques for Semantic Segmentation (2019, background): “It has been addressed even before the rise of neural networks popularity [6, 25, 33], but more recently it has been rediscovered and tackled in different ways.”
Large Scale Incremental Learning (2019, background): “Incremental learning has been a long standing problem in machine learning [3, 17, 16, 12].”
Incremental Learning of Object Detectors without Catastrophic Forgetting (2017, background): “The problem of incremental learning has a long history in machine learning and artificial intelligence [6,29,36,37].”
Incremental Classifier Learning with Generative Adversarial Networks (2018, background): “Related Work Incremental learning has been a long standing problem in machine learning [3, 20, 17, 12].”
Towards Making Systems Forget with Machine Unlearning (2015, methodology): “While prior work proposed incremental machine learning for several specific learning algorithms [31, 62, 73], the key difference in our work is that we propose a general efficient unlearning approach applicable to any algorithm that can be converted to the summation form, including some that…”