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Online Co-regularized Algorithms
2012We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks and a real world natural language processing dataset.
Ruijter, T. de +2 more
openaire +3 more sources
2012
We show that a commonly-used sampling theoretical attribute discretization algorithm ChiMerge can be implemented efficiently in the online setting. Its benefits include that it is efficient, statistically justified, robust to noise, can be made to produce low-arity partitions, and has empirically been observed to work well in practice.
Saarela Matti +2 more
openaire +1 more source
We show that a commonly-used sampling theoretical attribute discretization algorithm ChiMerge can be implemented efficiently in the online setting. Its benefits include that it is efficient, statistically justified, robust to noise, can be made to produce low-arity partitions, and has empirically been observed to work well in practice.
Saarela Matti +2 more
openaire +1 more source
Mathematical Programming, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the training of a neural network for online path planning with offline path planning algorithms
International Journal of Information Management, 2021Inkyung Sung +2 more
exaly
Online Passive-Aggressive Multilabel Classification Algorithms
IEEE Transactions on Neural Networks and Learning Systems, 2023Tingting Zhai, Hao Wang
exaly
Online force-directed algorithms for visualization of dynamic graphs
Information Sciences, 2021Se-Hang Cheong +2 more
exaly