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MP
2008
126views more  MP 2008»
13 years 7 months ago
Geometry of interpolation sets in derivative free optimization
We consider derivative free methods based on sampling approaches for nonlinear optimization problems where derivatives of the objective function are not available and cannot be dir...
Andrew R. Conn, Katya Scheinberg, Luís N. V...
AI
2002
Springer
13 years 7 months ago
The size distribution for Markov equivalence classes of acyclic digraph models
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
Steven B. Gillispie, Michael D. Perlman
AI
2010
Springer
13 years 7 months ago
Understanding the scalability of Bayesian network inference using clique tree growth curves
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
Ole J. Mengshoel
BMCBI
2007
123views more  BMCBI 2007»
13 years 7 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...
CSDA
2007
264views more  CSDA 2007»
13 years 7 months ago
Model-based methods to identify multiple cluster structures in a data set
Model-based clustering exploits finite mixture models for detecting group in a data set. It provides a sound statistical framework which can address some important issues, such as...
Giuliano Galimberti, Gabriele Soffritti