Add to ORKGKnowledge discovery in databases has become an increas-ingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this paper is how to scale machine learning algorithms, that typically are designed to deal with main memory based datasets, to efficiently learn from large distributed databases. We have explored an approach called meta-learning that is related to the traditional approaches of data reduction com-monly employed in distributed query processing systems. Here we seek efficient means to learn how to combine a number of base classifiers, which are learned from subsets of the data, so that we scale efficiently to larger learning problems, and boost the accuracy of the constit...
Interest in distributed approaches to machine learning has increased significantly in recent years d...
Combiner and Stacked Generalization are two very similar meta-learning methods that combine predicti...
In this paper, we propose a new approach for apply-ing data mining techniques, and more particularly...
In this paper we study the issue of how to scale machine learning algorithms, that typically are des...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
There has been considerable interest recently in various approaches to scaling up machine learning s...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
In data mining, the selection of appropriate classifier to estimate some unknown attribute of a new ...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
JAM is a powerful and portable agent-based distributed data mining system that employs meta-learning...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
In the paper, we propose a new approach to applying meta-learning concepts to parallel data mining. ...
We consider the problem of computing machine learning models over multi-relational databases. The ma...
This paper presents a novel machine learning algorithm with an improved accuracy and a faster learni...
Interest in distributed approaches to machine learning has increased significantly in recent years d...
Combiner and Stacked Generalization are two very similar meta-learning methods that combine predicti...
In this paper, we propose a new approach for apply-ing data mining techniques, and more particularly...
In this paper we study the issue of how to scale machine learning algorithms, that typically are des...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
There has been considerable interest recently in various approaches to scaling up machine learning s...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
In data mining, the selection of appropriate classifier to estimate some unknown attribute of a new ...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
JAM is a powerful and portable agent-based distributed data mining system that employs meta-learning...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
Much of the research in inductive learning concentrates on problems with relatively small amounts of...
In the paper, we propose a new approach to applying meta-learning concepts to parallel data mining. ...
We consider the problem of computing machine learning models over multi-relational databases. The ma...
This paper presents a novel machine learning algorithm with an improved accuracy and a faster learni...
Interest in distributed approaches to machine learning has increased significantly in recent years d...
Combiner and Stacked Generalization are two very similar meta-learning methods that combine predicti...
In this paper, we propose a new approach for apply-ing data mining techniques, and more particularly...