HIGHLIGHTS
- who: Hiroto Kuramata and Hideki Yagi from the Department of Computer and Network Engineering, The University of Electro-Communications have published the research: Analysis on Optimal Error Exponents of Binary Classification for Source with Multiple Subclasses, in the Journal: Entropy 2022, 24, 635. of /2022/
- what: The authors investigate the first- and second-order maximum error exponents under the constraint that the type-I error probability for all pairs of distributions decays exponentially fast and the type-II error probability is upper bounded by a small constant. The authors investigate the binary classification problem for . . .
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