Results 251 to 260 of about 6,527,322 (289)
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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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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.
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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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A Noncoherent Incremental Learning Demodulator
2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020Incremental learning after deployment is one of several attractive capabilities that motivate the use of neural network demodulators. This paper presents a complex noncoherent neural network suitable for on-off key (OOK) demodulation. When trained in an AWGN channel, the demodulator learns a solution that outperforms the traditional noncoherent matched
Paul Gorday +2 more
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Incremental Dictionary Learning With Sparsity
2018 International Joint Conference on Neural Networks (IJCNN), 2018Dictionary learning methods, such as K-SVD, have recently been introduced to learn a specific dictionary matrix that best fits a set of training data vectors. These methods are flexible in that any preferred pursuit method of sparse coding can be used to represent the data.
Mahmood R. Azimi-Sadjadi +2 more
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A new scheme for incremental learning
Neural Processing Letters, 1995We present a new incremental procedure for supervised learning with noisy data. Each step consists in adding to the current network a new unit which is trained to learn the error of the network. The incremental step is repeated until the error of the current network can be considered as a noise. The stopping criterion is very simple and can be directly
Christian Jutten, Rachida Chentouf
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Meta-learning for Fast Incremental Learning
2003Model based learning systems usually face to a problem of forgetting as a result of the incremental learning of new instances. Normally, the systems have to re-learn past instances to avoid this problem. However, the re-learning process wastes substantial learning time.
Takayuki Oohira +2 more
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Incremental learning of logic programs
1995In this paper, we identify a class of polynomial-time learnable logic programs. These programs can be learned from examples in an incremental fashion using the already defined predicates as background knowledge. Our class properly contains the class of innermost simple programs of [20] and the class of hereditary programs of [12,13].
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Multiagent Incremental Learning in Networks
2008This paper investigates incremental multiagent learning in structured networks. Learning examples are incrementally distributed among the agents, and the objective is to build a common hypothesis that is consistent with all the examples present in the system, despite communication constraints.
Bourgne, Gauvain +3 more
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Incremental Learning of People Identities
2019Face recognition in unconstrained open-world settings is a challenging problem. Differently from the closed-set and open-set face recognition scenarios that assume that the face representations of known subjects have been manually enrolled in a gallery, the open-world scenario requires that the system learns identities incrementally from frame to frame,
Bartoli F. +3 more
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