On-line learning in pattern classification using active sampling
نویسندگان
چکیده
An adaptive on-line learning method is presented to faciliate pattern classi cation using active sampling to identify optimal decision boundary for a stochastic oracle with minimum number of training samples. The strategy of sampling at the current estimate of the decision boundary is shown to be optimal in the sense that the probability of convergence toward the true decision boundary at each step is maximized, o ering theoretical justi cation on the popular strategy of category boundary sampling used by many query learning algorithms. Analysis of convergence in distribution is formulated using the Markov chain model.
منابع مشابه
On - Line Learning for Active Pattern
| An adaptive on-line learning method is presented to faciliate pattern classiication using active sampling to identify optimal decision boundary for a stochas-tic oracle with 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 the probability of convergence toward the ...
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تاریخ انتشار 1997