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An adaptive on-line learning method is presented to facilitate pattern classification using active sampling to identify the optimal decision boundary for a stochastic oracle with a minimum number of training samples. The strategy of sampling at the current estimate of the decision boundary is shown to be optimal compared to random sampling in the sense that(More)
  • Jong-Min Park
  • 2004
The analysis of convergence and its application is shown for the Active Sampling-at-the-Boundary method applied to multidimensional space using orthogonal pillar vectors. Active learning method facilitates identifying an optimal decision boundary for pattern classification in machine learning. The result of this method is compared with the standard active(More)
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