ICANN
1997
Springer
14 years 3 months ago
1997
Springer
A new boosting algorithm ADABOOST-R for regression problems is presented and upper bound on the error is obtained. Experimental results to compare ADABOOST-R and other learning alg...
KDD
1998
ACM
14 years 3 months ago
1998
ACM
This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments ...
COLT
1998
Springer
14 years 3 months ago
1998
Springer
Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform conve...
KDD
1999
ACM
14 years 3 months ago
1999
ACM
Tree Augmented Naive Bayes (TAN) has shown to be competitive with state-of-the-art machine learning algorithms [3]. However, the TAN induction algorithm that appears in [3] can be...
COLT
1999
Springer
14 years 3 months ago
1999
Springer
We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the ...
ECML
2001
Springer
14 years 4 months ago
2001
Springer
When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these...
COLT
2001
Springer
14 years 4 months ago
2001
Springer
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...
KDD
2009
ACM
14 years 4 months ago
2009
ACM
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
ICDCS
2002
IEEE
14 years 4 months ago
2002
IEEE
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
ECML
2003
Springer
14 years 4 months ago
2003
Springer
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. Through analyzin...