Sciweavers

ICANN
1997
Springer
13 years 7 months ago
A Boosting Algorithm for Regression
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...
Alberto Bertoni, Paola Campadelli, M. Parodi
KDD
1998
ACM
120views Data Mining» more  KDD 1998»
13 years 7 months ago
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution
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 ...
Tim Oates, David Jensen
COLT
1998
Springer
13 years 7 months ago
Self Bounding Learning Algorithms
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...
Yoav Freund
KDD
1999
ACM
152views Data Mining» more  KDD 1999»
13 years 7 months ago
Applying General Bayesian Techniques to Improve TAN Induction
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...
Jesús Cerquides
COLT
1999
Springer
13 years 8 months ago
Drifting Games
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 ...
Robert E. Schapire
ECML
2001
Springer
13 years 8 months ago
Comparing the Bayes and Typicalness Frameworks
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...
Thomas Melluish, Craig Saunders, Ilia Nouretdinov,...
COLT
2001
Springer
13 years 8 months ago
Smooth Boosting and Learning with Malicious Noise
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 ...
Rocco A. Servedio
KDD
2009
ACM
224views Data Mining» more  KDD 2009»
13 years 8 months ago
Issues in evaluation of stream learning algorithms
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...
João Gama, Raquel Sebastião, Pedro P...
ICDCS
2002
IEEE
13 years 8 months ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
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 ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...
ECML
2003
Springer
13 years 8 months ago
Ensembles of Multi-instance Learners
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...
Zhi-Hua Zhou, Min-Ling Zhang