TY - GEN

T1 - Supervised graph inference

AU - Vert, Jean Philippe

AU - Yamanishi, Yoshihiro

PY - 2005

Y1 - 2005

N2 - We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network reconstruction from genomic data.

AB - We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network reconstruction from genomic data.

UR - http://www.scopus.com/inward/record.url?scp=84898969241&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898969241&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84898969241

SN - 0262195348

SN - 9780262195348

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004

PB - Neural information processing systems foundation

T2 - 18th Annual Conference on Neural Information Processing Systems, NIPS 2004

Y2 - 13 December 2004 through 16 December 2004

ER -