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» Generalization Bounds for Learning Kernels
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TEC
2012
197views Formal Methods» more  TEC 2012»
11 years 10 months ago
Improving Generalization Performance in Co-Evolutionary Learning
Recently, the generalization framework in co-evolutionary learning has been theoretically formulated and demonstrated in the context of game-playing. Generalization performance of...
Siang Yew Chong, Peter Tino, Day Chyi Ku, Xin Yao
EUROCOLT
1995
Springer
14 years 4 hour ago
A decision-theoretic generalization of on-line learning and an application to boosting
k. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multi...
Yoav Freund, Robert E. Schapire
ICML
2009
IEEE
14 years 9 months ago
PAC-Bayesian learning of linear classifiers
We present a general PAC-Bayes theorem from which all known PAC-Bayes risk bounds are obtained as particular cases. We also propose different learning algorithms for finding linea...
Alexandre Lacasse, François Laviolette, Mar...
ALT
2004
Springer
14 years 5 months ago
On Kernels, Margins, and Low-Dimensional Mappings
Kernel functions are typically viewed as providing an implicit mapping of points into a high-dimensional space, with the ability to gain much of the power of that space without inc...
Maria-Florina Balcan, Avrim Blum, Santosh Vempala
TIT
2002
164views more  TIT 2002»
13 years 8 months ago
On the generalization of soft margin algorithms
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...
John Shawe-Taylor, Nello Cristianini