New Perspectives on the Complexity of Computational Learning, and Other
... of reductions may be useful to prove equivalence of NP-hardness and the hardness of PAC learning, or equivalence of the non-triviality of ZK and the hardness of PAC learning. A more detailed overview of these results may be found in Chapter 2. In Chapter 6 we also apply the methodology of studying r ...
... of reductions may be useful to prove equivalence of NP-hardness and the hardness of PAC learning, or equivalence of the non-triviality of ZK and the hardness of PAC learning. A more detailed overview of these results may be found in Chapter 2. In Chapter 6 we also apply the methodology of studying r ...
Randomness on computable probability spaces—a dynamical point
... 2. compressibility. This characterization of random sequences, due to Schnorr and Levin (see [18, 11]), uses the prefix-free Kolmogorov complexity: random sequences are those which are maximally complex. 3. predictability. In this approach (started by Ville [14] and reintroduced to the modern theory ...
... 2. compressibility. This characterization of random sequences, due to Schnorr and Levin (see [18, 11]), uses the prefix-free Kolmogorov complexity: random sequences are those which are maximally complex. 3. predictability. In this approach (started by Ville [14] and reintroduced to the modern theory ...