Active LearningSimon Tong, Daphne Koller
Active Image Retrieval Demo
The standard framework in Machine Learning presents the learner with a randomly sampled data set. There has been growing interest in the area of Active Learning. Here, one allows the learner the flexibility to choose the data points that it feels are most relevant for learning a particular task. One analogy is that a standard passive learner is a student that sits and listens to a teacher while an active learner is a student that asks the teacher questions, listens to the answers and asks further questions based upon the teacher's repsonse. We are currently investigating techniques for performing active learning in three widely applicable situations: classification, density estimation and discovering causal structure. Our current results show that active learners using these techniques can outperfom regular passive learners substantially.
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