Add to ORKGInternational audienceIn this paper we propose a general framework to study the generalization properties of binary classifiers trained with data which may be dependent, but are deterministically generated upon a sample of independent examples. It provides generalization bounds for binary classification and some cases of ranking problems, and clarifies the relationship between these learning tasks
This paper is concerned with generalization issues for a decision tree learner for structured data c...
In this work, we construct generalization bounds to understand existing learning algorithms and prop...
Takes initial steps toward a theory of generalization and learning in the learning classifier system...
In this paper we propose a general framework to study the generalization properties of binary classi...
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
We investigate the generalizability of learned binary relations: functions that map pairs of instanc...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
AbstractThe studies of generalization error give possible approaches to estimate the performance of ...
AbstractWe consider the generalization error of concept learning when using a fixed Boolean function...
Abstract The generalization error, or probability of misclassification, of ensemble classifiers has ...
This paper analyzes the predictive performance of standard techniques for the 'logical analysis of d...
This paper surveys certain developments in the use of probabilistic techniques for the modelling of ...
This paper concerns learning binary-valued functions defined on, and investigates how a particular t...
International audienceThis paper deals with the generalization ability of classifiers trained from n...
In this paper, we focus the attention on one of the oldest problems in pattern recognition and machi...
This paper is concerned with generalization issues for a decision tree learner for structured data c...
In this work, we construct generalization bounds to understand existing learning algorithms and prop...
Takes initial steps toward a theory of generalization and learning in the learning classifier system...
In this paper we propose a general framework to study the generalization properties of binary classi...
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
We investigate the generalizability of learned binary relations: functions that map pairs of instanc...
The generalization error, or probability of misclassification, of ensemble classifiers has been show...
AbstractThe studies of generalization error give possible approaches to estimate the performance of ...
AbstractWe consider the generalization error of concept learning when using a fixed Boolean function...
Abstract The generalization error, or probability of misclassification, of ensemble classifiers has ...
This paper analyzes the predictive performance of standard techniques for the 'logical analysis of d...
This paper surveys certain developments in the use of probabilistic techniques for the modelling of ...
This paper concerns learning binary-valued functions defined on, and investigates how a particular t...
International audienceThis paper deals with the generalization ability of classifiers trained from n...
In this paper, we focus the attention on one of the oldest problems in pattern recognition and machi...
This paper is concerned with generalization issues for a decision tree learner for structured data c...
In this work, we construct generalization bounds to understand existing learning algorithms and prop...
Takes initial steps toward a theory of generalization and learning in the learning classifier system...