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Incremental Zero-Shot Learning
IEEE Transactions on Cybernetics, 2022The goal of zero-shot learning (ZSL) is to recognize objects from unseen classes correctly without corresponding training samples. The existing ZSL methods are trained on a set of predefined classes and do not have the ability to learn from a stream of training data.
Kun Wei +3 more
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2009
Data mining and knowledge discovery is about creating a comprehensible model of the data. Such a model may take different forms going from simple association rules to complex reasoning system. One of the fundamental aspects this model has to fulfill is adaptivity.
Xin Geng, Kate Smith-Miles
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Data mining and knowledge discovery is about creating a comprehensible model of the data. Such a model may take different forms going from simple association rules to complex reasoning system. One of the fundamental aspects this model has to fulfill is adaptivity.
Xin Geng, Kate Smith-Miles
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Incremental backpropagation learning networks
IEEE Transactions on Neural Networks, 1996How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules
L, Fu, H H, Hsu, J C, Principe
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Leveraging joint incremental learning objective with data ensemble for class incremental learning
Neural Networks, 2023A class-incremental learning problem is characterized by training data becoming available in a phase-by-phase manner. Deep learning models suffer from catastrophic forgetting of the classes in the older phases as they get trained on the classes introduced in the new phase.
Pratik Mazumder +3 more
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Properties of incremental projection learning
Neural Networks, 2001We proposed a method of incremental projection learning which provides exactly the same generalization capability as that obtained by batch projection learning in the previous paper. However, properties of the method have not yet been investigated. In this paper, we analyze its properties from the following aspects: First, it is shown that some of the ...
Masashi Sugiyama, Hidemitsu Ogawa
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Incremental Learning From Stream Data
IEEE Transactions on Neural Networks, 2011Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative ...
He, Haibo +3 more
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Identity Recognition by Incremental Learning
2018Face recognition systems nowadays benefit from the improved performance of new classification models combined with the availability of large datasets of face images and the increase of computational power.
del Bimbo A. +3 more
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Incremental Machine Learning: Incremental Classification
2022 7th International Conference on Computer Science and Engineering (UBMK), 2022Engin Baysal, Cüneyt Bayılmış
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1990
Chapters 3 through 5 have described how incremental version-space merging can be used to learn incrementally from a sequence of training instances. More generally, however, the information processed by incremental version-space merging need not correspond directly training data; as long as a piece of information can be converted into a version space of
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Chapters 3 through 5 have described how incremental version-space merging can be used to learn incrementally from a sequence of training instances. More generally, however, the information processed by incremental version-space merging need not correspond directly training data; as long as a piece of information can be converted into a version space of
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2006
Learning with adaptivity is a key issue in many nowadays applications. The most important aspect of such an issue is incremental learning (IL). This latter seeks to equip learning algorithms with the ability to deal with data arriving over long periods of time. Once used during the learning process, old data is never used in subsequent learning stages.
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Learning with adaptivity is a key issue in many nowadays applications. The most important aspect of such an issue is incremental learning (IL). This latter seeks to equip learning algorithms with the ability to deal with data arriving over long periods of time. Once used during the learning process, old data is never used in subsequent learning stages.
openaire +1 more source