Add to ORKGActive learning has been proven to be quite effec-tive in reducing the human labeling efforts by ac-tively selecting the most informative examples to label. In this paper, we present a batch-mode ac-tive learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in lo-gistic regression conditioned on any fixed train-ing sample. It is different from a typical PAC-style passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be imme-diately used to design an active learning algo-rithm by minimizing this bound iteratively. We also discuss the...
Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Inste...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effo...
Which active learning methods can we expect to yield good performance in learning logistic regressio...
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effo...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
Which of the many proposed methods for active learning can we expect to yield good performance in ...
Active learning techniques have gained popularity to reduce human effort in labeling data instances ...
Logistic regression is by far the most widely used classifier in real-world applications. In this pa...
We propose a method for dynamic batch mode active learning where the batch size and selection criter...
This paper presents an active learning method that di-rectly optimizes expected future error. This i...
The goal of active learning is to select the most informative examples for manual labeling. Most of ...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
Active learning techniques have gained popularity to reduce human effort in labeling data instances ...
Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Inste...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effo...
Which active learning methods can we expect to yield good performance in learning logistic regressio...
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effo...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
Which of the many proposed methods for active learning can we expect to yield good performance in ...
Active learning techniques have gained popularity to reduce human effort in labeling data instances ...
Logistic regression is by far the most widely used classifier in real-world applications. In this pa...
We propose a method for dynamic batch mode active learning where the batch size and selection criter...
This paper presents an active learning method that di-rectly optimizes expected future error. This i...
The goal of active learning is to select the most informative examples for manual labeling. Most of ...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
Active learning techniques have gained popularity to reduce human effort in labeling data instances ...
Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Inste...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
Over the last decade there has been growing interest in pool-based active learning techniques, where...