Add to ORKGThe use of multivariate classifiers, especially neural networks and decision trees, has become com-monplace in particle physics. Typically, a series of classifiers is trained rather than just one to en-hance the performance; this is known as boosting. This paper presents a novel method of boosting that produces a uniform selection efficiency in a user-defined multivariate space. Such a technique is ideally suited for amplitude analyses or other situations where optimizing a single integrated figure of merit is not what is desired. ar X i
. Classifier learning is a key technique for KDD. Approaches to learning classifier committees, incl...
Boosting is a general approach for improving classifier performances. In this research we investigat...
Boosting is a general approach for improving classifier performances. In this research we investigat...
ABSTRACT: The use of multivariate classifiers has become commonplace in particle physics. To enhance...
The use of multivariate classifiers has become commonplace in particle physics. To enhance the perfo...
The use of multivariate classifiers, especially neural networks and decision trees, has become commo...
International audienceMachine learning algorithms are growing increasingly popular in particle physi...
Machine learning algorithms are growing increasingly popular in particle physics analyses, where the...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
This thesis introduces new approaches, namely the DataBoost and DataBoost-IM algorithms, to extend B...
The common approach for constructing a classifier for particle selection assumes reasonable consiste...
International audienceDecision trees are a machine learning technique more and more commonly used in...
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
Multivariate discrimination or classification is one of the best-studied problem in machine learning...
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
. Classifier learning is a key technique for KDD. Approaches to learning classifier committees, incl...
Boosting is a general approach for improving classifier performances. In this research we investigat...
Boosting is a general approach for improving classifier performances. In this research we investigat...
ABSTRACT: The use of multivariate classifiers has become commonplace in particle physics. To enhance...
The use of multivariate classifiers has become commonplace in particle physics. To enhance the perfo...
The use of multivariate classifiers, especially neural networks and decision trees, has become commo...
International audienceMachine learning algorithms are growing increasingly popular in particle physi...
Machine learning algorithms are growing increasingly popular in particle physics analyses, where the...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
This thesis introduces new approaches, namely the DataBoost and DataBoost-IM algorithms, to extend B...
The common approach for constructing a classifier for particle selection assumes reasonable consiste...
International audienceDecision trees are a machine learning technique more and more commonly used in...
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
Multivariate discrimination or classification is one of the best-studied problem in machine learning...
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
. Classifier learning is a key technique for KDD. Approaches to learning classifier committees, incl...
Boosting is a general approach for improving classifier performances. In this research we investigat...
Boosting is a general approach for improving classifier performances. In this research we investigat...