Transductive Inference with Graphs
2004
Technical Report
ei
We propose a general regularization framework for transductive inference. The given data are thought of as a graph, where the edges encode the pairwise relationships among data. We develop discrete analysis and geometry on graphs, and then naturally adapt the classical regularization in the continuous case to the graph situation. A new and effective algorithm is derived from this general framework, as well as an approach we developed before.
Author(s): | Zhou, D. and Schölkopf, B. |
Year: | 2004 |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Technical Report (techreport) |
Institution: | Max Planck Institute for Biological Cybernetics |
Note: | See the improved version Regularization on Discrete Spaces. |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
BibTex @techreport{2828, title = {Transductive Inference with Graphs}, author = {Zhou, D. and Sch{\"o}lkopf, B.}, organization = {Max-Planck-Gesellschaft}, institution = {Max Planck Institute for Biological Cybernetics}, school = {Biologische Kybernetik}, year = {2004}, note = {See the improved version Regularization on Discrete Spaces.}, doi = {} } |