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ICML
1998
IEEE
16 years 4 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 3 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...
101
Voted
AIPS
2006
15 years 4 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
132
Voted
ICML
2006
IEEE
16 years 4 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
15 years 9 months 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