Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction Luheng He Jennifer Gillenwater Computer and Information Science University of Pennsylvania

First Page		Document Content
Date: 2013-06-12 16:10:52 Conditional random field Machine learning Theoretical computer science Derivative Expectation–maximization algorithm Laplace operator Dimensional analysis Graph partition Mathematical analysis Mathematics Calculus		Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction Luheng He Jennifer Gillenwater Computer and Information Science University of Pennsylvania Add to Reading List Source URL: homes.cs.washington.edu Download Document from Source Website File Size: 342,34 KB Share Document on Facebook

	Spectral clustering is a well-known way to partition a graph or network into clusters or communities with provable guarantees on the quality of the clusters. This guarantee is known as the Cheeger inequality and it holds DocID: 1rFa4 - View Document
	A Fresh Look on Knowledge Bases: Distilling Named Events from News Erdal Kuzey Jilles Vreeken DocID: 1rpJU - View Document
	Spectral graph theory and its applications Daniel A. Spielman Spectral graph theory—the study of the eigenvectors and eigenvalues of matrices associated with graphs—is a large field with many beautiful results. Most DocID: 1r9xi - View Document
	Aydın Buluc¸ C ONTACT I NFORMATION Lawrence Berkeley National Laboratory. Mailstop 59R4104 DocID: 1r6qI - View Document
	Sparse Matrix-Matrix Multiplication: Applications, Algorithms, and Implementations Organizers: Grey Ballard and Alex Druinsky SIAM Conference on Applied Linear Algebra October 26, 2015 DocID: 1r1mf - View Document