Suchergebnisse - "Active Learning algorithm"

Greedy active learning algorithm for logistic regression models von Hsu, Hsiang-Ling, Chang, Yuan-chin Ivan, Chen, Ray-Bing

“… We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method …”
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An AUC-based Active Learning Algorithm via LogitBoost for Binary Classification von Zhang, Zhe-Bin, Wang, Charlotte

“… When labeling subjects is quite expensive, to incorporate both labeled and unlabeled data in training a classifier becomes a common strategy …”
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Greedy Active Learning Algorithm for Logistic Regression Models von Hsiang-Ling Hsu, Yuan-Chin, Ivan Chang, Ray-Bing, Chen

“… We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method …”
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Towards User‐Centered Active Learning Algorithms von Bernard, Jürgen, Zeppelzauer, Matthias, Lehmann, Markus, Müller, Martin, Sedlmair, Michael

“… The labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive …”
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Integrating active learning and crowdsourcing into large-scale supervised landcover mapping algorithms von Debats, Stephanie R, Estes, Lyndon D, Thompson, David R, Caylor, Kelly K

“… Sub-Saharan Africa and other developing regions of the world are dominated by smallholder farms, which are characterized by small, heterogeneous, and often …”
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A Study of Multimodal AI and Human Feedback Loops in STEM Learning Environments von D'Souza, Karen Michelle

“… The study explores the integration of artificial intelligence (AI) systems in a pedagogical dynamic collaborative tool namely cyber peer-led team learning …”
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Deep Active Learning for Surface Defect Classification Based on Informativeness and Diversity von Powell, Steven

“… This motivates the induction of using deep learning models for defect classification. However, there is still a great dependency upon subject matter experts in such labeling efforts. Active learning (AL …”
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Research on Pretreatment of Questions Based on Large-scale Real Questions Set von Wan, Qingsheng, Huang, Shaobin, Wei, Mengxi

“… The method uses KNN-based active learning algorithm to train the classifier, and uses the neighborhood category system to classify the questions efficiently …”
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Deep Active Learning with Relative Label Feedback: An Application to Facial Age Estimation von Singh, Ankita, Chakraborty, Shayok

“… Deep learning has emerged as an effective machine learning algorithm to automatically learn a representative set of features and has revolutionized multimedia …”
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Complexity Reduction for Near Real-Time High Dimensional Filtering and Estimation Applied to Biological Signals von Gupta, Manish

“… Real-time processing of physiological signals collected from wearable sensors that can be done with low computational power is a requirement for continuous …”
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Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning von Debats, Stephanie Renee

“… Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often …”
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On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise von Shen, Jie

“… most \(\nu\). Our main contribution is a Perceptron-like online active learning algorithm that runs in polynomial time, and under the conditions that the marginal distribution is isotropic log-concave and \(\nu = \Omega(\epsilon)\), where \(\epsilon \in (0, 1 …”
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Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation von Sharma, Manali, Das, Kamalika, Bilgic, Mustafa, Matthews, Bryan, Nielsen, David Lynn, Oza, Nikunj C

“… In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish …”
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Efficient active learning of sparse halfspaces von Zhang, Chicheng

“… a \(t\)-sparse halfspace that performs well on the data (\(t \ll d\)), we would like our active learning algorithm …”
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Active Learning for Crowdsourced QoE Modeling von Chang, Haw-Shiuan, Hsu, Chih-Fan, Hosfeld, Tobias, Chen, Kuan-Ta

“… , vol. 24, no. 2, pp. 36-41, Mar./Apr.2010.] toward a multidimensional QoS-QoE model (MIQX). To explore the complicated interaction between QoS factors more efficiently, we develop active learning algorithms for the multidimensional …”
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Near-Optimal Bayesian Active Learning with Noisy Observations von Golovin, Daniel, Krause, Andreas, Ray, Debajyoti

“… We develop EC2, a novel, greedy active learning algorithm and prove that it is competitive with the optimal policy, thus obtaining the first competitiveness guarantees for Bayesian active learning …”
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Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces von Potter, Thomas, Nguyen, Thinh, Luo, Zhizeng, Chen, Kang, She, Qingshan, Zhang, Yingchun

“… In this paper, a novel active learning method is proposed to minimize the amount of labeled, subject-specific EEG data required for effective classifier training, by combining measures of uncertainty …”
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Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks von Joshi, Ajjen, Ghosh, Soumya, Betke, Margrit, Sclaroff, Stan, Pfister, Hanspeter

“… Finally, we investigate active learning algorithms for interactively labeling personalization data in resource-constrained scenarios …”
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Deep Active Learning with Noisy Oracle in Object Detection von Schubert, Marius, Riedlinger, Tobias, Kahl, Karsten, Rottmann, Matthias

“… Reducing the amount of annotations required while maintaining algorithm performance is, therefore, desirable for machine learning practitioners and has been successfully achieved by active learning algorithms …”
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Building Adaptive Computational Systems for Physiological and Biomedical Data via Transfer and Active Learning von Chattopadhyay, Rita

“… The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data …”
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