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COLT
1999
Springer
14 years 2 months ago
Regret Bounds for Prediction Problems
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
Geoffrey J. Gordon
IJON
2007
184views more  IJON 2007»
13 years 10 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
IJCAI
2007
14 years 2 days ago
Simple Training of Dependency Parsers via Structured Boosting
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...
Qin Iris Wang, Dekang Lin, Dale Schuurmans
FGR
2008
IEEE
240views Biometrics» more  FGR 2008»
14 years 5 months ago
Face recognition and alignment using support vector machines
Face recognition in the presence of pose changes remains a largely unsolved problem. Severe pose changes, resulting in dramatically different appearances, is one of the main dif...
Antony Lam, Christian R. Shelton
CORR
2000
Springer
126views Education» more  CORR 2000»
13 years 10 months ago
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
We investigate the performance of two machine learning algorithms in the context of antispam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a nee...
Ion Androutsopoulos, Georgios Paliouras, Vangelis ...