Exponentiated Gradient Exploration for Active Learning
نویسندگان
چکیده
منابع مشابه
Exponentiated Gradient Exploration for Active Learning
Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named exponentiated gr...
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We consider two algorithms for on-line prediction based on a linear model. The algorithms are the well-known gradient descent (GD) algorithm and a new algorithm, which we call EG. They both maintain a weight vector using simple updates. For the GD algorithm, the update is based on subtracting the gradient of the squared error made on a prediction. The EG algorithm uses the components of the gra...
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ژورنال
عنوان ژورنال: Computers
سال: 2016
ISSN: 2073-431X
DOI: 10.3390/computers5010001