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» Learning to rank using gradient descent
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ICML
2007
IEEE
16 years 1 months ago
Winnowing subspaces
We generalize the Winnow algorithm for learning disjunctions to learning subspaces of low rank. Subspaces are represented by symmetric projection matrices. The online algorithm ma...
Manfred K. Warmuth
108
Voted
CORR
2010
Springer
105views Education» more  CORR 2010»
15 years 23 days ago
Online Identification and Tracking of Subspaces from Highly Incomplete Information
This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation), an efficient online algorithm for tracking subspaces from highly incomplete observations. GROUSE requi...
Laura Balzano, Robert Nowak, Benjamin Recht
PRIS
2004
15 years 2 months ago
Neural Network Learning: Testing Bounds on Sample Complexity
Several authors have theoretically determined distribution-free bounds on sample complexity. Formulas based on several learning paradigms have been presented. However, little is kn...
Joaquim Marques de Sá, Fernando Sereno, Lu&...
88
Voted
IJCAI
1997
15 years 2 months ago
An Effective Learning Method for Max-Min Neural Networks
Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ m...
Loo-Nin Teow, Kia-Fock Loe
97
Voted
NIPS
2001
15 years 2 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr