Add to ORKGActive learning traditionally relies on instance based utility measures to rank and select instances for labeling, which may result in labeling redundancy. To address this issue, we explore instance utility from two dimensions: individual uncertainty and instance disparity, using a correlation matrix. The active learning is transformed to a semi-definite programming problem to select an optimal subset with maximum utility value. Experiments demonstrate the algorithm performance in comparison with baseline approaches
We study the problem of combining active learning suggestions to identify informative training examp...
In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to t...
An "active learning system" will sequentially decide which unlabeled instance to label, with the goa...
Active learning traditionally relies on instance based utility measures to rank and select instances...
Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertain...
Active learning aims to train an accurate prediction model with minimum cost by labeling most inform...
In many classification problems unlabelled data is abundant and a subset can be chosen for labelling...
Active Learning is the problem of interactively constructing the training set used in classifica-tio...
The field of Machine Learning is concerned with the development of algorithms, models and techniques...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask ...
Hasenjäger M. Active data selection in supervised and unsupervised learning. Bielefeld: Bielefeld Un...
In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is all...
With active learning the learner participates in the process of selecting instances so as to speed-u...
Abstract. An improved active learning method taking advantage of feature selection technique is prop...
We study the problem of combining active learning suggestions to identify informative training examp...
In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to t...
An "active learning system" will sequentially decide which unlabeled instance to label, with the goa...
Active learning traditionally relies on instance based utility measures to rank and select instances...
Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertain...
Active learning aims to train an accurate prediction model with minimum cost by labeling most inform...
In many classification problems unlabelled data is abundant and a subset can be chosen for labelling...
Active Learning is the problem of interactively constructing the training set used in classifica-tio...
The field of Machine Learning is concerned with the development of algorithms, models and techniques...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask ...
Hasenjäger M. Active data selection in supervised and unsupervised learning. Bielefeld: Bielefeld Un...
In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is all...
With active learning the learner participates in the process of selecting instances so as to speed-u...
Abstract. An improved active learning method taking advantage of feature selection technique is prop...
We study the problem of combining active learning suggestions to identify informative training examp...
In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to t...
An "active learning system" will sequentially decide which unlabeled instance to label, with the goa...