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Non‐Noble Metal Nanocatalysts for Hydrogen Evolution
Advanced Science, EarlyView.Recent overviews and latest developments of diverse classes of non‐noble catalysts for application toward high‐performance photocatalysis, thermocatalytic steam reforming, and electrocatalysis. Toward sustainability objectives, several advanced characterization tools in combination with the latest breakthroughs in machine learning for material ...
Lina Jaya Diguna +7 more
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On the Strength of Incremental Learning
1999This paper provides a systematic study of incremental learning from noise-free and from noisy data, thereby distinguishing between learning from only positive data and from both positive and negative data. Our study relies on the notion of noisy data introduced in [22]. The basic scenario, named iterative learning, is as follows.
Steffen Lange, Gunter Grieser
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Incremental Learning By Decomposition
2006 5th International Conference on Machine Learning and Applications (ICMLA'06), 2006Adaptivity in neural networks aims at equipping learning algorithms with the ability to self-update as new training data becomes available. In many application, data arrives over long periods of time, hence the traditional one-shot training phase cannot be applied. The most appropriate training methodology in such circumstances is incremental learning (
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On Appropriate Refractoriness and Weight Increment in Incremental Learning
2013Neural networks are able to learn more patterns with the incremental learning than with the correlative learning. The incremental learning is a method to compose an associate memory using a chaotic neural network. The capacity of the network is found to increase along with its size which is the number of the neurons in the network and to be larger than
Toshinori Deguchi +2 more
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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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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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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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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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