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ICML
2006
IEEE
16 years 6 months ago
On a theory of learning with similarity functions
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
Maria-Florina Balcan, Avrim Blum
177
Voted
ML
2008
ACM
110views Machine Learning» more  ML 2008»
15 years 4 months ago
A theory of learning with similarity functions
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
Maria-Florina Balcan, Avrim Blum, Nathan Srebro
ICML
2004
IEEE
16 years 6 months ago
Learning first-order rules from data with multiple parts: applications on mining chemical compound data
Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorl...
Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numa...
RECOMB
2005
Springer
16 years 5 months ago
Learning Interpretable SVMs for Biological Sequence Classification
Background: Support Vector Machines (SVMs) ? using a variety of string kernels ? have been successfully applied to biological sequence classification problems. While SVMs achieve ...
Christin Schäfer, Gunnar Rätsch, Sö...
145
Voted
CRYPTO
2010
Springer
151views Cryptology» more  CRYPTO 2010»
15 years 6 months ago
Leakage-Resilient Pseudorandom Functions and Side-Channel Attacks on Feistel Networks
Abstract. A cryptographic primitive is leakage-resilient, if it remains secure even if an adversary can learn a bounded amount of arbitrary information about the computation with e...
Yevgeniy Dodis, Krzysztof Pietrzak