JCST
2006
13 years 4 months ago
2006
Abstract In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. This pa...
IJCAI
2003
13 years 6 months ago
2003
In this paper we propose the framework of Monte Carlo algorithms as a useful one to analyze ensemble learning. In particular, this framework allows one to guess when bagging will ...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filt...
ECML
2004
Springer
13 years 10 months ago
2004
Springer
The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image...
MCS
2005
Springer
13 years 10 months ago
2005
Springer
We describe an ensemble learning approach that accurately learns from data that has been partitioned according to the arbitrary spatial requirements of a large-scale simulation whe...
ICTAI
2006
IEEE
13 years 10 months ago
2006
IEEE
We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of ...
ICPR
2008
IEEE
13 years 11 months ago
2008
IEEE
Ensemble learning is a variational Bayesian method in which an intractable distribution is approximated by a lower-bound. Ensemble learning results in models with better generaliz...
MCS
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from...