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Improving generalization with active learning

by David Cohn, Richard Ladner, Alex Waibel - Machine Learning , 1994
"... Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples ..."
Abstract - Cited by 544 (1 self) - Add to MetaCart
Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples

Active Learning with Statistical Models

by David A. Cohn, Zoubin Ghahramani, Michael I. Jordan , 1995
"... For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statist ..."
Abstract - Cited by 679 (10 self) - Add to MetaCart
, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

Neural network ensembles, cross validation, and active learning

by Anders Krogh, Jesper Vedelsby - Neural Information Processing Systems 7 , 1995
"... Learning of continuous valued functions using neural network en-sembles (committees) can give improved accuracy, reliable estima-tion of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members aver-aged over unlabeled data, so it qua ..."
Abstract - Cited by 479 (6 self) - Add to MetaCart
Learning of continuous valued functions using neural network en-sembles (committees) can give improved accuracy, reliable estima-tion of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members aver-aged over unlabeled data, so

Support Vector Machine Active Learning with Applications to Text Classification

by Simon Tong , Daphne Koller - JOURNAL OF MACHINE LEARNING RESEARCH , 2001
"... Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based acti ..."
Abstract - Cited by 735 (5 self) - Add to MetaCart
-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which

Support vector machine active learning for image retrieval

by Simon Tong , 2001
"... Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user’s desired output or query concept by asking the user whether certain proposed images ..."
Abstract - Cited by 456 (28 self) - Add to MetaCart
are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user’s query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance

Active learning literature survey

by Burr Settles , 2010
"... The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., ..."
Abstract - Cited by 326 (1 self) - Add to MetaCart
The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e

Does active learning work? A review of the research

by Michael Prince - J. Engr. Education
"... This study examines the evidence for the effectiveness of active learning. It defines the common forms of active learning most relevant for engineering faculty and critically examines the core element of each method. It is found that there is broad but uneven support for the core elements of active, ..."
Abstract - Cited by 311 (8 self) - Add to MetaCart
This study examines the evidence for the effectiveness of active learning. It defines the common forms of active learning most relevant for engineering faculty and critically examines the core element of each method. It is found that there is broad but uneven support for the core elements of active

Active learning: Creating excitement in the classroom

by Charles C. Bonwell, Ph. D , 1991
"... "College teaching and lecturing have been so long associated that when one pictures a college professor in a classroom, he almost inevitably pictures him as lecturing. " Few would argue with the statement that the vast majority of today's professoriate were primarily lectured to as bo ..."
Abstract - Cited by 275 (0 self) - Add to MetaCart
students from passive listeners to active learners. On first glance, like many of the recommendations provided by "blue ribbon panels, " this would seem "easier said than done." The incorporation of active learning strategies into the daily routine of classroom instruction can

Toward Optimal Active Learning through Sampling Estimation of Error Reduction

by Nicholas Roy, Andrew Mccallum - In Proc. 18th International Conf. on Machine Learning , 2001
"... This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expec ..."
Abstract - Cited by 353 (2 self) - Add to MetaCart
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation

Employing EM in Pool-Based Active Learning for Text Classification

by Andrew Mccallum, Kamal Nigam , 1998
"... This paper shows how a text classifier's need for labeled training data can be reduced by a combination of active learning and Expectation Maximization (EM) on a pool of unlabeled data. Query-by-Committee is used to actively select documents for labeling, then EM with a naive Bayes model furthe ..."
Abstract - Cited by 320 (10 self) - Add to MetaCart
This paper shows how a text classifier's need for labeled training data can be reduced by a combination of active learning and Expectation Maximization (EM) on a pool of unlabeled data. Query-by-Committee is used to actively select documents for labeling, then EM with a naive Bayes model
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