Add to ORKGWe describe NRC's submission to the Anomaly Detection/Text Mining competition organised at the Text Mining Workshop 2007. This submission relies on a straightforward implementation of the probabilistic categoriser described in (Gaussier et al., ECIR'02). This categoriser is adapted to handle multiple labelling and a piecewise-linear confidence estimation layer is added to provide an estimate of the labelling confidence. This technique achieves a score of 1.689 on the test data
This study presents a review of the recent advances in performing inference in probabilistic classif...
Machine learning studies algorithms for learning from data. Probabilistic modeling and reasoning...
In multi-class text classification, the performance (effectiveness) of a classifier is usually measu...
We describe NRC's submission to the Anomaly Detection/Text Mining competition organised at the Text ...
We describe the National Research Council's (NRC) entry in the Anomaly Detection/Text Mining competi...
In \ac{ATC}, a general inductive process automatically builds a classifier for the categories involv...
Nous abordons le problème de la classification non supervisée de documents par des méthodes probabil...
Text categorization is the classification of documents with respect to a set of predefined categorie...
This paper explores the use of a statistical technique known as density estimation to potentially im...
This paper gives the system description of the neural probabilistic language modeling (NPLM) team of...
Copyright © 2004 Springer Verlag. The final publication is available at link.springer.com5th Interna...
Text classification is the task of assigning predefined categories to free text documents. Due to th...
This paper experimentally reports on Bayesian predictive uncertainty for real-world text classificat...
Text data mining is the process of extracting and analyzing valuable information from text. A text d...
Quantitative characterizations and estimations of uncertainty are of fundamental importance for mach...
This study presents a review of the recent advances in performing inference in probabilistic classif...
Machine learning studies algorithms for learning from data. Probabilistic modeling and reasoning...
In multi-class text classification, the performance (effectiveness) of a classifier is usually measu...
We describe NRC's submission to the Anomaly Detection/Text Mining competition organised at the Text ...
We describe the National Research Council's (NRC) entry in the Anomaly Detection/Text Mining competi...
In \ac{ATC}, a general inductive process automatically builds a classifier for the categories involv...
Nous abordons le problème de la classification non supervisée de documents par des méthodes probabil...
Text categorization is the classification of documents with respect to a set of predefined categorie...
This paper explores the use of a statistical technique known as density estimation to potentially im...
This paper gives the system description of the neural probabilistic language modeling (NPLM) team of...
Copyright © 2004 Springer Verlag. The final publication is available at link.springer.com5th Interna...
Text classification is the task of assigning predefined categories to free text documents. Due to th...
This paper experimentally reports on Bayesian predictive uncertainty for real-world text classificat...
Text data mining is the process of extracting and analyzing valuable information from text. A text d...
Quantitative characterizations and estimations of uncertainty are of fundamental importance for mach...
This study presents a review of the recent advances in performing inference in probabilistic classif...
Machine learning studies algorithms for learning from data. Probabilistic modeling and reasoning...
In multi-class text classification, the performance (effectiveness) of a classifier is usually measu...