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NIPS
2001
13 years 9 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Roni Khardon, Dan Roth, Rocco A. Servedio
CVPR
2007
IEEE
14 years 9 months ago
Practical Online Active Learning for Classification
We compare the practical performance of several recently proposed algorithms for active learning in the online classification setting. We consider two active learning algorithms (...
Claire Monteleoni, Matti Kääriäinen
ICML
2010
IEEE
13 years 8 months ago
Online Learning for Group Lasso
We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional bat...
Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Ly...
COLT
2006
Springer
13 years 11 months ago
Online Variance Minimization
We design algorithms for two online variance minimization problems. Specifically, in every trial t our algorithms get a covariance matrix Ct and try to select a parameter vector wt...
Manfred K. Warmuth, Dima Kuzmin
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
13 years 12 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang