Add to ORKGPrediction intervals for class probabilities are of interest in machine learning because they can quantify the uncertainty about the class probability estimate for a test instance. The idea is that all likely class probability values of the test instance are included, with a pre-specified confidence level, in the calculated prediction interval. This thesis proposes a probabilistic model for calculating such prediction intervals. Given the unobservability of class probabilities, a Bayesian approach is employed to derive a complete distribution of the class probability of a test instance based on a set of class observations of training instances in the neighbourhood of the test instance. A random decision tree ensemble learning algorithm is a...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
peer reviewedThe ultimate goal of a one-class classifier like the “rigorous” soft independent modeli...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
Prediction intervals for class probabilities are of interest in machine learning because they can qu...
Combining multiple classifiers can give substantial improvement in prediction performance of learnin...
In a Bayesian framework, to make predictions on a sequence $X_1,X_2,ldots$ of random observations, t...
In a Bayesian framework, to make predictions on a sequence $X_1,X_2,ldots$ of random observations, t...
Machine learning methods can be used for estimating the class membership probability of an observati...
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
In Bayesian classifier learning, estimating the joint probability distribution (,) or the likelihood...
Generally, the unknown coefficients of neural nets are estimated by nonlinear least squares. Therefo...
International audienceClassification models usually associate one class for each new instance. This ...
We propose a method for producing ensembles of predictors based on holdout estimations of their gene...
none4noIn a Bayesian framework, to make predictions on a sequence $X_1,X_2,ldots$ of random observat...
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, noviembre de 2...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
peer reviewedThe ultimate goal of a one-class classifier like the “rigorous” soft independent modeli...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
Prediction intervals for class probabilities are of interest in machine learning because they can qu...
Combining multiple classifiers can give substantial improvement in prediction performance of learnin...
In a Bayesian framework, to make predictions on a sequence $X_1,X_2,ldots$ of random observations, t...
In a Bayesian framework, to make predictions on a sequence $X_1,X_2,ldots$ of random observations, t...
Machine learning methods can be used for estimating the class membership probability of an observati...
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
In Bayesian classifier learning, estimating the joint probability distribution (,) or the likelihood...
Generally, the unknown coefficients of neural nets are estimated by nonlinear least squares. Therefo...
International audienceClassification models usually associate one class for each new instance. This ...
We propose a method for producing ensembles of predictors based on holdout estimations of their gene...
none4noIn a Bayesian framework, to make predictions on a sequence $X_1,X_2,ldots$ of random observat...
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, noviembre de 2...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
peer reviewedThe ultimate goal of a one-class classifier like the “rigorous” soft independent modeli...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...