| Paper ID | D7-S6-T3.3 |
| Paper Title |
Active Binary Classification of Random Fields |
| Authors |
Arpan Mukherjee, Ali Tajer, Rensselaer Polytechnic Institute, United States; Pin-Yu Chen, Payel Das, IBM Thomas J. Watson Research Center, United States |
| Session |
D7-S6-T3: Testing |
| Chaired Session: |
Tuesday, 20 July, 23:40 - 00:00 |
| Engagement Session: |
Wednesday, 21 July, 00:00 - 00:20 |
| Abstract |
Consider a sequence of $n$ random variables $\bX=(X_1,\cdots,X_n)$ forming a random field (RF). $\bX$ is assumed to be generated according to one of the two possible classes of probability measures $\mcP\triangleq \big\{\P_i:i\in\{1,\cdots,m\}\big\}$ and $\mcQ\triangleq \big\{\mathbb{Q}_i:i\in\{1,\cdots,m\}\big\}$. There are upto $s$ realizations of the random variable $X_i$, for $i\in\{1,\cdots,m\}$. This paper addresses the following two questions : 1) Given a target classification reliability, what is the minimum number of samples required (out of $ns$) to classify $\bX$? 2) What is the optimal sampling order? This paper addresses these questions in the asymptote of large $n$.
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