Add to ORKGUnlike previous comparative studies, we present an empirical evaluation on three typical statistical ensemble methods, boosting [1], bagging [2] and combination of weak perceptrons [3], in terms of speaker identification where miscellaneous mismatch conditions are involved. During creating an ensemble, moreover, different combination strategies are also investigated. As a result, our studies present their generalization capabilities on mismatch conditions, which provides alternative insight to understand those methods.Computer Science, Artificial IntelligenceComputer Science, Theory & MethodsEngineering, Electrical & ElectronicCPCI-S(ISTP)
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
Over the years, countless algorithms have been proposed to solve the problem of speech enhancement f...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
To improve the performance of the telephone-line speaker identification system, we take in use three...
This paper investigates a number of ensemble methods for improv-ing the performance of phoneme class...
Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemb...
Statistical ensemble learning methods have turned to be effective way to improve accuracy of a learn...
We address the question of whether and how boosting and bagging can be used for speech recognition. ...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
An ensemble consists of a set of individually trained classifiers (such as neural networks or decisi...
We address the question of whether and how boosting and bagging can be used for speech recognition....
Recent expansions of technology led to growth and availability of different types of data. This, thu...
In this paper we make an extensive study of different methods for building ensembles of classifiers....
Bagging and boosting are among the most popular resampling ensemble methods that generate and combin...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
Over the years, countless algorithms have been proposed to solve the problem of speech enhancement f...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...
To improve the performance of the telephone-line speaker identification system, we take in use three...
This paper investigates a number of ensemble methods for improv-ing the performance of phoneme class...
Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemb...
Statistical ensemble learning methods have turned to be effective way to improve accuracy of a learn...
We address the question of whether and how boosting and bagging can be used for speech recognition. ...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
An ensemble consists of a set of individually trained classifiers (such as neural networks or decisi...
We address the question of whether and how boosting and bagging can be used for speech recognition....
Recent expansions of technology led to growth and availability of different types of data. This, thu...
In this paper we make an extensive study of different methods for building ensembles of classifiers....
Bagging and boosting are among the most popular resampling ensemble methods that generate and combin...
Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more acc...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
Over the years, countless algorithms have been proposed to solve the problem of speech enhancement f...
bootstrapping, resampling. Using an ensemble of classifiers, instead of a single classifier, can lea...