Exponential Families for Conditional Random Fields

In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited efficiently in the optimization process.

• Publication year

  2004

• Venue

  Conference on Uncertainty in Artificial Intelligence

• Publication date

  2004-07-07

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 1207.4131
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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