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AAAI
2007
14 years 9 days ago
Knowledge-Driven Learning and Discovery
The goal of our current research is machine learning with the help and guidance of a knowledge base (KB). Rather than learning numerical models, our approach generates explicit sy...
Benjamin Lambert, Scott E. Fahlman
CVPR
2005
IEEE
14 years 12 months ago
Robust Boosting for Learning from Few Examples
We present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of our original data. The motivation is that since the learning algori...
Lior Wolf, Ian Martin
JMLR
2008
95views more  JMLR 2008»
13 years 10 months ago
Learning Similarity with Operator-valued Large-margin Classifiers
A method is introduced to learn and represent similarity with linear operators in kernel induced Hilbert spaces. Transferring error bounds for vector valued large-margin classifie...
Andreas Maurer
JMLR
2010
84views more  JMLR 2010»
13 years 4 months ago
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these mo...
Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj...
EUROMICRO
1997
IEEE
14 years 2 months ago
What computer architecture can learn from computational intelligence-and vice versa
This paper considers whether the seemingly disparate fields of Computational Intelligence (CI) and computer architecture can profit from each others’ principles, results and e...
Ronald Moore, Bernd Klauer, Klaus Waldschmidt