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JACM
2006
99views more  JACM 2006»
13 years 10 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
PAMI
2008
198views more  PAMI 2008»
13 years 10 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation....
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...
EC
2000
187views ECommerce» more  EC 2000»
13 years 10 months ago
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function i...
Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele
ICML
2007
IEEE
14 years 11 months ago
Least squares linear discriminant analysis
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. LDA in the binaryclass case has been shown to be equivalent to linear re...
Jieping Ye
ICML
2006
IEEE
14 years 11 months ago
Null space versus orthogonal linear discriminant analysis
Dimensionality reduction is an important pre-processing step for many applications. Linear Discriminant Analysis (LDA) is one of the well known methods for supervised dimensionali...
Jieping Ye, Tao Xiong