Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information

  title={Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information},
  author={Deniz Erdogmus and Jos{\'e} Carlos Pr{\'i}ncipe},
  journal={VLSI Signal Processing},
Fano’s inequality has proven to be one important result in Shannon’s information theory having found applications in innumerous proofs of convergence. It also provides us with a lower bound on the symbol error probability in a communication channel, in terms of Shannon’s definitions of entropy and mutual information. This result is also significant in that it suggests insights on how the classification performance is influenced by the amount of information transferred through the classifier. We… CONTINUE READING
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