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Active Learning and Transfer Learning for Document Segmentation
AbstractIn this paper, we investigate the effectiveness of classical approaches to active learning in the problem of document segmentation with the...
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Active learning for data streams: a survey
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The...
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WMBAL: weighted minimum bounds for active learning
In the present study, aimed at reliably acquiring difficult samples for object detection models from massive raw data, we propose a novel difficult...
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Regression tree-based active learning
Machine learning algorithms often require large training sets to perform well, but labeling such large amounts of data is not always feasible, as in...
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Evidential uncertainty sampling strategies for active learning
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and...
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Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies
Since requirement dependency extraction is a cognitively challenging and error-prone task, this paper proposes an automatic requirement dependency...
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Active learning-based hyperspectral image classification: a reinforcement learning approach
In the last few years, deep neural networks have been successful in classifying hyperspectral images (HSIs). However, training deep neural networks...
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Online concept evolution detection based on active learning
Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient...
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Enhancing network intrusion detection by lifelong active online learning
Machine learning has been widely used to build intrusion detection models in detecting unknown attack traffic. How to train a model properly in order...
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Active Selection Transfer Learning Algorithm
Transfer learning has the ability to utilize the knowledge of the source domain with enough available and labeled data to help build a learning model...
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Active learning algorithm through the lens of rejection arguments
Active learning is a paradigm of machine learning which aims at reducing the amount of labeled data needed to train a classifier. Its overall...
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Hyperspectral image classification via active learning and broad learning system
Hyperspectral image (HSI) classification has continued to be a hot research topic in recent years, and the broad learning system (BLS) has been...
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Uncertainty-aware complementary label queries for active learning
In this paper, we tackle the problem of ALCL (Liu et al., 2023). The objective of ALCL is to directly reduce the cost of annotation actions in AL,...
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Partial Image Active Annotation (PIAA): An Efficient Active Learning Technique Using Edge Information in Limited Data Scenarios
Active learning (AL) algorithms are increasingly being used to train models with limited data for annotation tasks. However, the selection of data...
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A Simple yet Effective Framework for Active Learning to Rank
While China has become the largest online market in the world with approximately 1 billion internet users, Baidu runs the world’s largest Chinese...
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Emergency events detection based on integration of federated learning and active learning
Social media networks now make it easy to access, in real-time, massive amounts of information from all over the world. They are often the primary...
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Active learning with biased non-response to label requests
Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However,...
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Active Learning by Extreme Learning Machine with Considering Exploration and Exploitation Simultaneously
As an important machine learning paradigm, active learning has been widely applied to scenarios in which it is easy to acquire a large number of...
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Efficient and robust active learning methods for interactive database exploration
There is an increasing gap between fast growth of data and the limited human ability to comprehend data. Consequently, there has been a growing...
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Active model learning of stochastic reactive systems (extended version)
Black-box systems are inherently hard to verify. Many verification techniques, like model checking, require formal models as a basis. However, such...
