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NIPS
2007
15 years 7 months ago
Convex Learning with Invariances
Incorporating invariances into a learning algorithm is a common problem in machine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbit...
Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex...
ICRA
2002
IEEE
115views Robotics» more  ICRA 2002»
15 years 11 months ago
DEEP: Dual-Space Expansion for Estimating Penetration Depth Between Convex Polytopes
Abstract— We present an incremental algorithm to estimate the penetration depth between convex polytopes in 3D. The algorithm incrementally seeks a “locally optimal solution”...
Young J. Kim, Ming C. Lin, Dinesh Manocha
CDC
2009
IEEE
117views Control Systems» more  CDC 2009»
15 years 10 months ago
Risk sensitive robust support vector machines
— We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. O...
Huan Xu, Constantine Caramanis, Shie Mannor, Sungh...
CVPR
2008
IEEE
16 years 8 months ago
Enforcing convexity for improved alignment with constrained local models
Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/tracking in comparison to leading holistic approaches (e.g., AAMs). A maj...
Yang Wang, Simon Lucey, Jeffrey F. Cohn
CCE
2004
15 years 5 months ago
Computational studies using a novel simplicial-approximation based algorithm for MINLP optimization
Significant advances have been made in the last two decades for the effective solution of mixed integer non-linear programming (MINLP) problems, mainly by exploiting the special s...
Vishal Goyal, Marianthi G. Ierapetritou