Part 6: Machine Learning-Learning (MALL)International audienceActive learning is an iterative supervised learning task where learning algorithms can actively query an oracle, i.e. a human annotator that understands the nature of the pro blem, for labels. As the learner is allowed to interactively choose the data from which it learns, it is expected that the learner will perform better with less training. The active learning approach is appropriate to machine learning applications where training labels are costly to obtain but unlabeled data is abundant. Although active learning has been widely considered for single-label learning, this is not the case for multi-label learning, where objects can have more than one class labels and a multi-la...
Abstract—Conventional active learning dynamically constructs the training set only along the sample ...
<p>Most classic machine learning methods depend on the assumption that humans can annotate all the d...
This dissertation develops and analyzes active learning algorithms for binary classification problem...
Obtaining labels can be expensive or time-consuming, but unlabeled data is often abundant and easier...
The key idea behind active learning is that a machine learning algorithm can achieve greater accurac...
In this paper, we address multi-labeler active learning, where data labels can be acquired from mult...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
Multi-label classification, where each instance is assigned to multiple categories, is a prevalent p...
Abstract — Conventional active learning dynamically con-structs the training set only along the samp...
Active learning is useful in situations where labeled data is scarce, unlabeled data is available an...
Active learning reduces the labeling cost by selec-tively querying the most valuable information fro...
Abstract—In multi-label learning, it is rather expensive to label instances since they are simultane...
Traditional supervised machine learning algorithms are expected to have access to a large corpus of ...
Multi-label active learning is an important problem because of the expensive labeling cost in multi-...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
Abstract—Conventional active learning dynamically constructs the training set only along the sample ...
<p>Most classic machine learning methods depend on the assumption that humans can annotate all the d...
This dissertation develops and analyzes active learning algorithms for binary classification problem...
Obtaining labels can be expensive or time-consuming, but unlabeled data is often abundant and easier...
The key idea behind active learning is that a machine learning algorithm can achieve greater accurac...
In this paper, we address multi-labeler active learning, where data labels can be acquired from mult...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
Multi-label classification, where each instance is assigned to multiple categories, is a prevalent p...
Abstract — Conventional active learning dynamically con-structs the training set only along the samp...
Active learning is useful in situations where labeled data is scarce, unlabeled data is available an...
Active learning reduces the labeling cost by selec-tively querying the most valuable information fro...
Abstract—In multi-label learning, it is rather expensive to label instances since they are simultane...
Traditional supervised machine learning algorithms are expected to have access to a large corpus of ...
Multi-label active learning is an important problem because of the expensive labeling cost in multi-...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
Abstract—Conventional active learning dynamically constructs the training set only along the sample ...
<p>Most classic machine learning methods depend on the assumption that humans can annotate all the d...
This dissertation develops and analyzes active learning algorithms for binary classification problem...