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JMLR
2010
147views more  JMLR 2010»
15 years 1 months ago
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
DEXA
2011
Springer
234views Database» more  DEXA 2011»
14 years 6 months ago
Learning Top-k Transformation Rules
Record linkage identifies multiple records referring to the same entity even if they are not bit-wise identical. It is thus an essential technology for data integration and data c...
Sunanda Patro, Wei Wang
188
Voted
COLT
2006
Springer
15 years 10 months ago
Efficient Learning Algorithms Yield Circuit Lower Bounds
We describe a new approach for understanding the difficulty of designing efficient learning algorithms. We prove that the existence of an efficient learning algorithm for a circui...
Lance Fortnow, Adam R. Klivans
GECCO
2005
Springer
141views Optimization» more  GECCO 2005»
16 years 8 days ago
Constructing good learners using evolved pattern generators
Self-organization of brain areas in animals begins prenatally, evidently driven by spontaneously generated internal patterns. The neural structures continue to develop postnatally...
Vinod K. Valsalam, James A. Bednar, Risto Miikkula...
ML
1998
ACM
15 years 6 months ago
On Restricted-Focus-of-Attention Learnability of Boolean Functions
In the k-Restricted-Focus-of-Attention (k-RFA) model, only k of the n attributes of each example are revealed to the learner, although the set of visible attributes in each example...
Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jac...