
SecondOrder Asymptotically Optimal Statistical Classification
Motivated by realworld machine learning applications, we analyze approx...
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SecondOrder Asymptotically Optimal Universal Outlying Sequence Detection with Reject Option
Motivated by practical machine learning applications, we revisit the out...
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Statistical Linear Models in Virus Genomic Alignmentfree Classification: Application to Hepatitis C Viruses
Viral sequence classification is an important task in pathogen detection...
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SecondOrder Asymptotics of Sequential Hypothesis Testing
We consider the classical sequential binary hypothesis testing problem i...
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Distributed Detection with Empirically Observed Statistics
We consider a binary distributed detection problem in which the distribu...
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DatumWise Classification: A Sequential Approach to Sparsity
We propose a novel classification technique whose aim is to select an ap...
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Data Interpolating Prediction: Alternative Interpretation of Mixup
Data augmentation by mixing samples, such as Mixup, has widely been used...
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Sequential Classification with Empirically Observed Statistics
Motivated by realworld machine learning applications, we consider a statistical classification task in a sequential setting where test samples arrive sequentially. In addition, the generating distributions are unknown and only a set of empirically sampled sequences are available to a decision maker. The decision maker is tasked to classify a test sequence which is known to be generated according to either one of the distributions. In particular, for the binary case, the decision maker wishes to perform the classification task with minimum number of the test samples, so, at each step, she declares that either hypothesis 1 is true, hypothesis 2 is true, or she requests for an additional test sample. We propose a classifier and analyze the typeI and typeII error probabilities. We demonstrate the significant advantage of our sequential scheme compared to an existing nonsequential classifier proposed by Gutman. Finally, we extend our setup and results to the multiclass classification scenario and again demonstrate that the variablelength nature of the problem affords significant advantages as one can achieve the same set of exponents as Gutman's fixedlength setting but without having the rejection option.
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