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MapReduce, which provides a simple, scalable and fault-tolerant framework, can be utilized for large scale learning.
Then we design a parallel ensemble of online sequential extreme learning machine PEOS-ELMM) algorithm based on MapReduce for large scale learning.
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There are now new opportunities to be had from combining the larger scale learning analytics approaches and open learner models that visualise learning or understanding.
What is exciting now is that with large-scale learning platforms, such as the Open University's FutureLearn, learning data does not have to remain merely local.
The dual problem (3) can be rapidly solved with the large-scale learning library LIBLINEAR [27].
Chen et al. introduce us to a new scalable strategy for large-scale learning in "A Robust SVM Classification Framework Using PSM for Multi-class Recognition".
As a matter of fact, the problem of estimating the parameters of optical flow techniques may be seen as a large-scale learning problem.
For structure-based classifiers, it is common to encode the molecular graph of a compound with binary fingerprints for large-scale learning tasks in cheminformatics.
Therefore, the recent works in large-scale learning classifiers have focused on building linear classifiers for large-scale visual classification tasks.
Distributed processing on Hadoop enables large-scale learning, and the goal of this paper is to profile tools that can do exactly that.
The results over 23 datasets show OLLAWV's superior performance in terms of accuracy, scalability, and model sparseness, making it suitable for large-scale learning.
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