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ICML
1998
IEEE
16 years 7 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
SIAMIS
2008
116views more  SIAMIS 2008»
15 years 6 months ago
Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization
This paper introduces a new approach to the problem of quantitative reconstruction of an object from few radiographic views. A mixed variable programming problem is formulated in ...
Mark A. Abramson, Thomas J. Asaki, J. E. Dennis, K...
AIPS
2006
15 years 7 months ago
Optimal Rectangle Packing: A Meta-CSP Approach
We present a new approach to optimal rectangle packing, an NP-complete problem that can be used to model many simple scheduling tasks. Recent attempts at incorporating artificial ...
Michael D. Moffitt, Martha E. Pollack
ICML
2006
IEEE
16 years 7 months ago
Active learning via transductive experimental design
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple conc...
Kai Yu, Jinbo Bi, Volker Tresp
ATAL
2007
Springer
16 years 19 days ago
Unifying distributed constraint algorithms in a BDI negotiation framework
This paper presents a novel, unified distributed constraint satisfaction framework based on automated negotiation. The Distributed Constraint Satisfaction Problem (DCSP) is one t...
Bao Chau Le Dinh, Kiam Tian Seow