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FMSD
2008
110views more  FMSD 2008»
13 years 8 months ago
Automatic symbolic compositional verification by learning assumptions
Abstract Compositional reasoning aims to improve scalability of verification tools by reducing the original verification task into subproblems. The simplification is typically base...
Wonhong Nam, P. Madhusudan, Rajeev Alur
CVPR
2012
IEEE
11 years 11 months ago
Non-negative low rank and sparse graph for semi-supervised learning
Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel no...
Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma...
ICDM
2006
IEEE
145views Data Mining» more  ICDM 2006»
14 years 2 months ago
Stability Region Based Expectation Maximization for Model-based Clustering
In spite of the initialization problem, the ExpectationMaximization (EM) algorithm is widely used for estimating the parameters in several data mining related tasks. Most popular ...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
14 years 9 months ago
Mining for proposal reviewers: lessons learned at the national science foundation
In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The appl...
Seth Hettich, Michael J. Pazzani
ATVA
2006
Springer
153views Hardware» more  ATVA 2006»
14 years 13 days ago
Learning-Based Symbolic Assume-Guarantee Reasoning with Automatic Decomposition
Abstract. Compositional reasoning aims to improve scalability of verification tools by reducing the original verification task into subproblems. The simplification is typically bas...
Wonhong Nam, Rajeev Alur