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NIPS
2007
13 years 9 months ago
Random Features for Large-Scale Kernel Machines
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Ali Rahimi, Benjamin Recht
GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
14 years 9 hour ago
Controlling overfitting with multi-objective support vector machines
Recently, evolutionary computation has been successfully integrated into statistical learning methods. A Support Vector Machine (SVM) using evolution strategies for its optimizati...
Ingo Mierswa
ICML
2001
IEEE
14 years 9 months ago
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Nicholas Roy, Andrew McCallum
ENTCS
2010
111views more  ENTCS 2010»
13 years 5 months ago
Fundamental Nano-Patterns to Characterize and Classify Java Methods
Fundamental nano-patterns are simple, static, binary properties of Java methods, such as ObjectCreator and Recursive. We present a provisional catalogue of 17 such nano-patterns. ...
Jeremy Singer, Gavin Brown, Mikel Luján, Ad...
ICML
2009
IEEE
14 years 9 months ago
Archipelago: nonparametric Bayesian semi-supervised learning
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a mode...
Ryan Prescott Adams, Zoubin Ghahramani