A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

A review of network location theory and models

Cataloged from PDF version of article.In this study, we review the existing literature on network location problems. The study has a broad scope that includes problems featuring desirable and undesirable facilities, point facilities and extensive facilities, monopolistic and competitive markets, and single or multiple objectives. Deterministic and stochastic models as well as robust models are covered. Demand data aggregation is also discussed. More than 500 papers in this area are reviewed and critical issues, research directions, and problem extensions are emphasized.Erdoğan, Damla SelinM.S

Applications of network optimization

Includes bibliographical references (p. 41-48).Ravindra K. Ahuja ... [et al.]

The optimal location of facilities on a network

Imperial Users onl

Minimax network location : theory and algorithms

Originally presented as the author's Ph. D. thesis, M.I.T. Dept. of Aeronautics and Astronautics, 1974August 1974Includes bibliographical references (leaves 122-126)For a given network let P and N denote the set of all points and the set of all nodes respectively. Let G and T denote a cyclic network and a tree network respectively and let m denote the number of centers available. The categorization scheme P N/P N/m/G T, where the first and second cells refer to the possible locations of centers and demand generating points respectively, provides for compact identification of a variety of minimax network location problems. This dissertation presents algorithms which efficiently solve all problems in this class--for example, P/P/m/G-for virtually any size of network. Moreover, tree problems can usually be solved manually. Methodologically, the tree-based results are graph-theoretic while the general case, formulated in a mathematical programming framework, leads to a highly efficient strategy for a class of massive generalized set covering problems