624 search results - page 51 / 125 » The Tradeoffs of Large Scale Learning |
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
We present a novel algorithm for computing a training set consistent subset for the nearest neighbor decision rule. The algorithm, called FCNN rule, has some desirable properties....
PAMI
2007
13 years 7 months ago
2007
We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifier...
ECTEL
2007
Springer
14 years 2 months ago
2007
Springer
This paper examines progress made in the development of formats for the exchange of questions, tests and results. It is argued that despite large investments by vendors and educati...
CVPR
2012
IEEE
12 years 1 months ago
2012
IEEE
Learning from streams of evolving and unbounded data is an important problem, for example in visual surveillance or internet scale data. For such large and evolving real-world data...