Supplementary Material to A PAC-Bayesian Approach forDomain Adaptation with Specialization to Linear Classifiers
نویسندگان
چکیده
In this document, Section 1 contains some lemmas used in subsequent proofs, Section 2 presents an extended proof of the bound on the domain disagreement disρ(DS , DT ) (Theorem 3 of the main paper), Section 3 introduces other PAC-Bayesian bounds for disρ(DS , DT ) and RPT (Gρ), Section 4 shows equations and implementation details about PBDA (our proposed learning algorithm for PAC-Bayesian DA tasks).
منابع مشابه
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers
We provide a first PAC-Bayesian analysis for domain adaptation (DA) which arises when the learning and test distributions differ. It relies on a novel distribution pseudodistance based on a disagreement averaging. Using this measure, we derive a PAC-Bayesian DA bound for the stochastic Gibbs classifier. This bound has the advantage of being directly optimizable for any hypothesis space. We spec...
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تاریخ انتشار 2013