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With the advancement of internet technology and sensor networks, tremendous amount of data have been generated beyond our imagination. These data contain valuable and possibly relevant information for various fields of applications. Learning these data online by using current neural learning techniques is not so simple due to many technical constraints including data overflow, uncontrollable learning epochs, arbitrary class drift, and dynamic imbalanced class ratio. Recently, we have been attempted to tackle this neural learning problem under the non-stationary environment. In this article, we summarize the new concept of One-Pass-Learning-and-Discard and also new structures, called Malleable Hyper-ellipsoid and Hyper-cylinder, of neural network recently introduced to cope with supervised as well as unsupervised learning under the constraints of data overflow, preserving polynomial time and space complexities of learning process, arbitrary class drift, life of data, and dynamic imbalanced class ratio. Both structures are rotatable, transposable, and expandable according to the distribution and location of data cluster.
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- Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints
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