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» Generalization Bounds for Learning Kernels
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COLT
2007
Springer
14 years 1 months ago
How Good Is a Kernel When Used as a Similarity Measure?
Recently, Balcan and Blum [1] suggested a theory of learning based on general similarity functions, instead of positive semi-definite kernels. We study the gap between the learnin...
Nathan Srebro
COLT
2008
Springer
13 years 9 months ago
Dimension and Margin Bounds for Reflection-invariant Kernels
A kernel over the Boolean domain is said to be reflection-invariant, if its value does not change when we flip the same bit in both arguments. (Many popular kernels have this prop...
Thorsten Doliwa, Michael Kallweit, Hans-Ulrich Sim...
CORR
2010
Springer
148views Education» more  CORR 2010»
13 years 2 months ago
A Unifying View of Multiple Kernel Learning
Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different for...
Marius Kloft, Ulrich Rückert, Peter L. Bartle...
NIPS
2007
13 years 8 months ago
Stability Bounds for Non-i.i.d. Processes
The notion of algorithmic stability has been used effectively in the past to derive tight generalization bounds. A key advantage of these bounds is that they are designed for spec...
Mehryar Mohri, Afshin Rostamizadeh
COLT
2005
Springer
14 years 26 days ago
Stability and Generalization of Bipartite Ranking Algorithms
The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in mac...
Shivani Agarwal, Partha Niyogi