Add to ORKGIn statistical pattern recognition, the principal task is to classify abstract data sets. Instead of using robust but computational expensive algorithms it is possible to combine `weak´ classifiers that can be employed in solving complex classification tasks. In this comparative study, we will examine the effectiveness of the commonly used hybrid schemes - especially those used for speech recognition problems - concentrating on cases which employ different combinations of classifiers
In this paper, a theoretical and experimental analysis of linear combiners for multiple classifier s...
Kirchhoff K, Bilmes J. Dynamic classifier combination using utterance-level confidence values. In: ...
In this paper we present how the classification results can be improved using a set of classifiers w...
In statistical pattern recognition, the principal task is to classify abstract data sets. Instead o...
In this course project, I will attempt to write up a sur-vey of combination methods used in speech r...
Classifier combination is a technique that often provides significant improvements in accuracy, and ...
An overview of the various ways that speech recognition can be improved by combining different appro...
Combining multiple estimators to obtain a more accurate final result is a well-known technique in st...
We develop a common theoretical framework for combining classifiers which use distinct pattern repre...
The paper is devoted to a comparative study of classifier combination methods, which have been succe...
This paper presents our results obtained by experimenting with different classifiers and ...
There are many classification tools that can be used for various NLP tasks, although none of them c...
The aim of this paper is to investigate the role of the apriori knowledge in the process of classif...
In this paper we present how the classification results can be improved using a set of classifiers w...
Abstract: Classifier combinations are effective techniques for difficult pattern recognition problem...
In this paper, a theoretical and experimental analysis of linear combiners for multiple classifier s...
Kirchhoff K, Bilmes J. Dynamic classifier combination using utterance-level confidence values. In: ...
In this paper we present how the classification results can be improved using a set of classifiers w...
In statistical pattern recognition, the principal task is to classify abstract data sets. Instead o...
In this course project, I will attempt to write up a sur-vey of combination methods used in speech r...
Classifier combination is a technique that often provides significant improvements in accuracy, and ...
An overview of the various ways that speech recognition can be improved by combining different appro...
Combining multiple estimators to obtain a more accurate final result is a well-known technique in st...
We develop a common theoretical framework for combining classifiers which use distinct pattern repre...
The paper is devoted to a comparative study of classifier combination methods, which have been succe...
This paper presents our results obtained by experimenting with different classifiers and ...
There are many classification tools that can be used for various NLP tasks, although none of them c...
The aim of this paper is to investigate the role of the apriori knowledge in the process of classif...
In this paper we present how the classification results can be improved using a set of classifiers w...
Abstract: Classifier combinations are effective techniques for difficult pattern recognition problem...
In this paper, a theoretical and experimental analysis of linear combiners for multiple classifier s...
Kirchhoff K, Bilmes J. Dynamic classifier combination using utterance-level confidence values. In: ...
In this paper we present how the classification results can be improved using a set of classifiers w...