Active learning using pre-clustering

By Hieu Tat Nguyen, Arnold M. W. Smeulders. International Conference on Machine Learning, 2004

785 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology

View Paper: https://www.semanticscholar.org/paper/7c5bae29a266a7a67228c0a9b051961bc48ed4db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Epidemiology

Citations in the corpus

Group-Based Active Learning of Classification Models (2017, methodology): “Some are based on the uncertainty of instances (Lewis and Catlett 1994); Some rely on informativeness (Bodó, Minier, and Csató 2011) or representativeness (Nguyen and Smeulders 2004) of instances.”
Learning Loss for Active Learning (2019, background): “The uncertainty approach [26, 19, 55, 52, 49, 4] defines and measures the quantity of uncertainty to select uncertain data points, while the diversity approach [45, 37, 15, 5] selects diverse data points that represent the whole distribution of the unlabeled pool.”
A Survey on Malware Detection Using Data Mining Techniques (2017, background): “…the data scarcity problem, optimize the learning beneﬁt from domain experts’ feedback, and reduce the cost of acquiring labeled examples for supervised learning, has been intensively studied in recent years [Lewis and Gale 1994; Muslea et al. 2006; Seung et al. 1992; Nguyen and Smeulders 2004].”
A Survey of Deep Active Learning (2020, background): “se a hybrid query strategy in a batch query, taking into account both the information volume and diversity of samples in either an explicit or implicit manner. Early Batch Mode Active Learning (BMAL) [20, 77, 111, 171, 173] algorithm performance is often excessively reliant on the measurement of sim”
Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images (2011, background): “Another approach to consider the diversity in the query function is the use of clustering [14], [15].”
Variational Adversarial Active Learning (2019, background): “evious work in this direction. Pool-based methods can be grouped into three major categories as follows: uncertainty-based methods [19, 51, 1], representation-based models [40], and their combination [53, 38]. Pool-based methods have been theoretically proven to be effective and achieve better perfo”
ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation (2019, neutral): “al [33] and Xu et al.”
Active Learning (2012, background): “One could also cluster the most informative instances after each iteration and query the most representative instances of those clusters (Nguyen and Smeulders, 2004).”
A survey on instance selection for active learning (2012, background): “Nguyen and Smeulders [37] proposed a representative active learning algorithm considering the prior distribution of the instance set.”
Active Learning Methods for Remote Sensing Image Classification (2009, background): “The first class of active learning methods relies on SVM specificities [15]–[17] and has been widely applied in environmental science for monitoring network optimization [18],”
A theory of transfer learning with applications to active learning (2012, background): “The goal of transfer learning is to leverage previous learning and experience to more efﬁciently learn novel, but related, concepts, compared to what would be possible without this prior experience.”
A brief introduction to weakly supervised learning (2018, background): “Approaches based on representativeness generally aim to exploit the cluster structure of unlabeled data, usually by a clustering method [10,11].”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “Active learning [37, 128, 102], semi-supervised learning [36, 15], interactive machine learning [18, 89, 7] and a set of application-specific systems [119, 125, 42, 131, 5, 14, 7] are some examples of work that enable communication from humans to machines while improving the way to represent machine”
Active and Semisupervised Learning for the Classification of Remote Sensing Images (2014, background): “Another approach to consider the diversity in the query function is the use of clustering [42], [43].”
Discriminative Batch Mode Active Learning (2007, background): “When the training process is hard or time consuming, this repeated retraining is inefficient.”