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Online Dictionary Learning with Confidence
2018 IEEE International Conference on Data Mining (ICDM), 2018Online dictionary learning has received intensive attention in signal processing field with streaming or dynamic data. Different from classical online dictionary learning methods that treat all atoms equally, in this paper, we present a novel online dictionary learning with a confidence parameter introduced on each of atoms.
Shan You, Chang Xu 0002, Chao Xu 0006
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Linking online dictionaries to Wikipedia
The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), 2015In a dictionary, a word can have different senses, and each sense is described by a gloss. However, a gloss is short and may not be satisfactorily informative to users. Meanwhile, mostly each word sense has a corresponding article in Wikipedia that contains rich information about the concept expressed by the word sense.
Vinh Q. Tran 0001, Tru H. Cao
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Online deep transferable dictionary learning
Pattern Recognition, 2021Abstract In real-world applications, large-scale unlabeled data usually becomes available gradually over time. Online learning is important to update models while preserving their historical knowledge. However, a time-varying distribution shift exists in incoming sequential data in online learning, resulting in a data cluster discrepancy between the ...
Sheng Wu, Ancong Wu, Wei-Shi Zheng 0001
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Online dictionaries: today and tomorrow
Lexicographica - International Annual for Lexicography / Internationales Jahrbuch Für Lexikographie, 2012Sven Tarp
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Online kernel dictionary learning
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015Efficient online algorithms are developed to perform dictionary learning (DL) for the features lifted to a high-dimensional space via nonlinear mapping. Inspired by recent works on batch kernelized DL with promising performance for real-world learning tasks, two kernel DL formulations are put forth, amenable to online processing.
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Slice-Based Online Convolutional Dictionary Learning
IEEE Transactions on Cybernetics, 2021Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While yielding superior results compared to traditional sparse coding methods on various signal and image processing tasks, most CDL methods have difficulties handling large data, because they have to ...
Yijie Zeng, Jichao Chen, Guang-Bin Huang
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An online algorithm for distributed dictionary learning
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015This paper proposes a novel algorithm for online distributed dictionary learning, where a set of nodes is requested to collectively estimate a common dictionary via sequentially received data vectors. At each time instance, in which a new datum becomes available, the sparse representation of the data with respect to the estimated dictionary is computed
Symeon Chouvardas +2 more
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Automated dictionary discovery for the online marketplace
Proceedings of the 2012 iConference, 2012Shopping online has become a prolific activity as the number of online vendors and consumers continue to rise each year. In 2009, almost $15 billion in goods and services were ordered online by Canadians [1]. About 53% of these consumers 'window shop' by doing product research before actually making a purchase.
Fei Chiang, Renée J. Miller
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Online dictionaries: expectations and demands [PDF]
, 2014 This chapter presents empirical findings on the question which criteria are making a good online dictionary using data on expectations and demands collected in the first study (N=684), completed with additional results from the second study (N=390) which
Koplenig, Alexander +1 more
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Online parameterized dictionary matching with one gap
Theoretical Computer Science, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Avivit Levy, B. Riva Shalom
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