'Bipartite matching problem' presentation slideshows

CS6234: Lecture 1

1.12k views • 68 slides

An Iterative Relaxation Technique for the NMR Backbone Assignment Problem

An Iterative Relaxation Technique for the NMR Backbone Assignment Problem. Wen-Lian Hsu Institute of Information Science Academia Sinica. Characteristics of Our Method. Model this as a constraint satisfaction problem Solve it using natural language parsing techniques

719 views • 62 slides

Network Flows

Network Flows. Chun-Ta, Yu Graduate Institute Information Management Dept. National Taiwan University. Chapter 12. ASSIGHMENTS AND MATCHINGS. Outline. Introduction Bipartite Cardinality Matching Problem Bipartite Weighted Matching Problem Stable Marriage Problem

627 views • 50 slides

Graph Matching

Graph Matching. Lecture 16: Nov 28. This Lecture. Graph matching is an important problem in graph theory. It has many applications and is the basis of more advanced problems. In this lecture we will cover two versions of graph matching problems. Stable matching Bipartite matching.

804 views • 46 slides

Bipartite Matching

Bipartite Matching. Lecture 8: Oct 7. This Lecture. Graph matching is an important problem in graph theory. It has many applications and is the basis of more advanced problems. In the last lecture we consider the stable matching problem. Today we will study the bipartite matching problem.

664 views • 39 slides

Theory of Computing

Theory of Computing. Lecture 12 MAS 714 Hartmut Klauck. Network Flows. A flow network is a directed graph G =(V,E ) with nonegative edge weights C(u,v ), called capacities nonedges have capacity 0 There is a source s and a sink t

418 views • 33 slides

Bipartite Matching

Bipartite Matching. Lecture 3: Jan 17. Bipartite Matching. A graph is bipartite if its vertex set can be partitioned into two subsets A and B so that each edge has one endpoint in A and the other endpoint in B. B. A. A matching M is a subset of edges so that

459 views • 20 slides

KBP2014 Entity Linking Scorer

KBP2014 Entity Linking Scorer. Xiaoman Pan, Qi Li and Heng Ji {panx2,liq7,jih}rpi.edu. Overview. We apply two steps to evaluate the KBP2014 Entity Discovery and Linking results. 1. Existing Entity Linking ( Wikification ) F-score 2. NIL Clustering score. Overview.

358 views • 18 slides