We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour ($k$nn), support vector machine (SVM) and linear discriminant analysis (LDA) class...
Abstract A common assumption in supervised machine learning is that the training exam-ples provided ...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
Labelling of data for supervised learning canbe costly and time-consuming and the riskof incorporati...
For multi-class classification under class-conditional label noise, we prove that the accuracy metri...
Labelling of data for supervised learning canbe costly and time-consuming and the riskof incorporati...
© 2012 IEEE. Traditional classification systems rely heavily on sufficient training data with accura...
In many real-world classification problems, the labels of training examples are randomly corrupted. ...
In this paper, we theoretically study the problem of binary classification in the presence of random...
In this paper, we theoretically study the problem of binary classification in the presence of random...
A common approach in positive-unlabeled learning is to train a classification model between labeled ...
This thesis addresses three challenge of machine learning: high-dimensional data, label noise and li...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
This paper presents a new approach to identifying and eliminating mislabeled training instances for ...
Abstract A common assumption in supervised machine learning is that the training exam-ples provided ...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
Labelling of data for supervised learning canbe costly and time-consuming and the riskof incorporati...
For multi-class classification under class-conditional label noise, we prove that the accuracy metri...
Labelling of data for supervised learning canbe costly and time-consuming and the riskof incorporati...
© 2012 IEEE. Traditional classification systems rely heavily on sufficient training data with accura...
In many real-world classification problems, the labels of training examples are randomly corrupted. ...
In this paper, we theoretically study the problem of binary classification in the presence of random...
In this paper, we theoretically study the problem of binary classification in the presence of random...
A common approach in positive-unlabeled learning is to train a classification model between labeled ...
This thesis addresses three challenge of machine learning: high-dimensional data, label noise and li...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
This paper presents a new approach to identifying and eliminating mislabeled training instances for ...
Abstract A common assumption in supervised machine learning is that the training exam-ples provided ...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...