<p>Performance was measured as the difference in the number of batches to achieve (A,B) 100% or (C,D) 90% accuracy between active and random learning. (A,C) Greedy Merge, (B,D) B-Clustering. Warmer colors indicate greater experiment savings with an active learner.</p
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...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
<p>(A) Each model design was evaluated with both active and random learners on two simulated 100 tar...
<p>The experiment consisted of 500 sample interventions, with an initial 1,000 sample observation. W...
Abstract. Active Learning (AL) methods seek to improve classifier per-formance when labels are expen...
<p>(A) An experiment is a combination of a target and a condition; observed experiments (filled circ...
Active learning refers to the settings in which a machine learning algorithm (learner) is able to s...
<p>The experiment consisted of 500 sample interventions, with an initial 500 sample observation. Whi...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...
Object classification by learning from data is a vast area of statistics and machine learning. Withi...
Abstract: Active learning refers to the settings in which a machine learn-ing algorithm (learner) is...
<p>Comparison of performance between the proposed active learning method versus uniformly sampling f...
<p>Mean learning rates for active (solid) and random (dashed) learners across structure learning met...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
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...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
<p>(A) Each model design was evaluated with both active and random learners on two simulated 100 tar...
<p>The experiment consisted of 500 sample interventions, with an initial 1,000 sample observation. W...
Abstract. Active Learning (AL) methods seek to improve classifier per-formance when labels are expen...
<p>(A) An experiment is a combination of a target and a condition; observed experiments (filled circ...
Active learning refers to the settings in which a machine learning algorithm (learner) is able to s...
<p>The experiment consisted of 500 sample interventions, with an initial 500 sample observation. Whi...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...
Object classification by learning from data is a vast area of statistics and machine learning. Withi...
Abstract: Active learning refers to the settings in which a machine learn-ing algorithm (learner) is...
<p>Comparison of performance between the proposed active learning method versus uniformly sampling f...
<p>Mean learning rates for active (solid) and random (dashed) learners across structure learning met...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
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...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
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