38 search results - page 2 / 8 » Boosting on Manifolds: Adaptive Regularization of Base Class... |
PAKDD
2000
ACM
13 years 11 months ago
2000
ACM
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
COLT
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. The regularization functional induced by the graph Laplacian of a random neighborhood graph based on the data is adaptive in two ways. First it adapts to an underlying ma...
ICPR
2010
IEEE
13 years 5 months ago
2010
IEEE
Abstract--Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection perfor...
HIS
2004
13 years 9 months ago
2004
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
KDD
2009
ACM
14 years 8 months ago
2009
ACM
We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...