Incremental learning with support vector machines

By Stefan Rüping. 2001

599 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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Citations in the corpus

IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019, background): “Incremental learning techniques are one possible solution to the scalability problem, where data is processed in parts, and the result combined so as to use less memory (Syed et al. 1999).”
An Empirical Investigation of the Role of Pre-training in Lifelong Learning (2021, background): “In contrast, the lifelong learning (Thrun and Mitchell, 1995; Thrun, 1996; Chen and Liu, 2018) or incremental learning (Solomonoff et al., 1989; Syed et al., 1999; Ruping, 2001) or never-ending learning (Mitchell et al.”
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).”
Incremental Learning in Online Scenario (2020, background): “One representative work is [24], which learns the new decision boundary by using support vectors that are learned from old data together with new data.”
Class-Incremental Domain Adaptation (2020, background): “truly facilitate model upgrade (e.g. adding new classes to the recognition model) thereby having a limited practical use-case. Another line of work consists of Class-Incremental (CI) learning methods [4,28,38,42,53] which aim at adding new classes to a trained model while preserving the performance ”
Brain-Inspired Model for Incremental Learning Using a Few Examples (2020, background): “For example, Ruping [37] creates an incremental learner by storing support vectors from previously learned classes and using a mix of old and new support vectors to classify new data.”
Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers (2018, methodology): “More complex methods are based on machine learning approaches such as support vector machines [19], neural networks [20] or random forests [21] and are in general computationally more demanding.”
The kernel recursive least-squares algorithm (2004, unknown): “In spite of claims to the contrary [33], it has been shown, both theoretically and empirically [2, 8], that the solutions provided by SVMs are not always maximally sparse.”
Learning drifting concepts: Example selection vs. example weighting (2004, unknown): “Syed et al. [29,30] describe an approach to incrementally learning support vector machines by compressing the training data from past batches to their support vectors and discarding the examples that are no support vectors.”
Incremental learning algorithms and applications (2016, methodology): “Some rely on heuristics, like retraining a model with all support vectors plus a new ”incremental” batch of data [35, 152], but without theoretical guarantees.”
Learning with Augmented Class by Exploiting Unlabeled Data (2014, background): “…dealing with the changing environments is the incremental learning, which mainly includes sub-branches of the example-incremental learning (E-IL) (Ruping 2001; Polikar et al. 2001; Fern and Givan 2003), the attribute-incremental learning (A-IL) (Vapnik, Vashist, and Pavlovitch 2009), the…”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, unknown): “In [116], [186], [187], the”
Initial Classifier Weights Replay for Memoryless Class Incremental Learning (2020, background): “Incremental learning is a longstanding topic in machine learning [8, 24] and has recently regained traction with the introduction of deep learning-based methods [2, 7, 22].”
Incremental Learning Using Conditional Adversarial Networks (2019, methodology): “Ruping [30] utilized the mixture of old support vectors and new examples to reduce the consumption of time and memory for incremental learning.”