← Learning Types
Machine Learning › Learning Types ›

Active Learning

1885 directly classified papers

Papers per year

Papers

Active Learning for Multi-Objective Optimization ICML 2013
Efficient Active Learning of Halfspaces: an Aggressive Approach ICML 2013
Adaptive Task Assignment for Crowdsourced Classification ICML 2013
Efficient Semi-supervised and Active Learning of Disjunctions ICML 2013
Generic Exploration and K-armed Voting Bandits ICML 2013
Activized Learning with Uniform Classification Noise ICML 2013
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion NIPS 2013
From Semi-supervised to Transfer Counting of Crowds ICCV 2013
Active Learning of an Action Detector from Untrimmed Videos ICCV 2013
Active Visual Recognition with Expertise Estimation in Crowdsourcing ICCV 2013
Joint Transfer and Batch-mode Active Learning ICML 2013
Breaking the Small Cluster Barrier of Graph Clustering ICML 2013
Active Learning for Interactive Visualization AISTATS 2013
Selective sampling algorithms for cost-sensitive multiclass prediction ICML 2013
Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem COLT 2013
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? NIPS 2013
Fine-Grained Crowdsourcing for Fine-Grained Recognition CVPR 2013
Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback CVPR 2013
Collaborative Active Learning of a Kernel Machine Ensemble for Recognition ICCV 2013
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking ACML 2013
Σ-Optimality for Active Learning on Gaussian Random Fields NIPS 2013
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising NIPS 2013
Statistical Active Learning Algorithms NIPS 2013
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting NIPS 2013
Buy-in-Bulk Active Learning NIPS 2013