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Results 1 - 10
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198,488
Support Vector Machine Active Learning with Applications to Text Classification
- 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
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Cited by 735 (5 self)
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instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive
Ensemble Methods in Machine Learning
- MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin ..."
Abstract
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Cited by 625 (3 self)
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Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging
Improving generalization with active learning
- 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
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Cited by 544 (1 self)
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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
, 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
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Cited by 679 (10 self)
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, 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.
PEER ASSESSMENT AS ACTIVE LEARNING METHOD
"... The importance of feedback in the student learning process is well understood among educational researchers (theorists) and teachers (practitioners). Its positive effects on the students learning and achievements have extensively been discussed in the theoretical research and illustrated by many em ..."
Abstract
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empirical studies. It is also recognized that student assessment of other students' work is a useful tool for activating student engagement in the learning process. This work presents the results of a pilot study investigating the attitudes toward peer-assessment of examination papers and exploring
Learning to predict by the methods of temporal differences
- MACHINE LEARNING
, 1988
"... This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predi ..."
Abstract
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Cited by 1521 (56 self)
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This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between
ATTITUDE OF TEACHERS TOWARDS THE USE OF ACTIVE LEARNING METHODS
"... This study was undertaken to find out the attitude of teachers towards the use of Active Learning methods at Bahir Dar University in Ethiopia. The subjects were 23 university teachers purposively selected from foreign language department at Bahir Dar University. Data about the subjects were collecte ..."
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This study was undertaken to find out the attitude of teachers towards the use of Active Learning methods at Bahir Dar University in Ethiopia. The subjects were 23 university teachers purposively selected from foreign language department at Bahir Dar University. Data about the subjects were
Active Appearance Models.
- IEEE Transactions on Pattern Analysis and Machine Intelligence,
, 2001
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations ..."
Abstract
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Cited by 2154 (59 self)
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AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations
An improved active learning method based on feature selection
"... Abstract. An improved active learning method taking advantage of feature selection technique is proposed. In early stages of active learning, the whole dataset is described using only the few key features, so that its overall distribution characteristic can be learned easily, reducing active learnin ..."
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Abstract. An improved active learning method taking advantage of feature selection technique is proposed. In early stages of active learning, the whole dataset is described using only the few key features, so that its overall distribution characteristic can be learned easily, reducing active
A Hardware System of Active Learning Method
"... The active learning method (ALM), proposed as a methodology of soft computing, simulates human learning and inferencing processes on the basis of fuzzy concepts. This paper presents a hardware implementation of ALM. ALM has processing engines called IDS, which are tasked with extracting useful infor ..."
Abstract
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Cited by 1 (0 self)
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The active learning method (ALM), proposed as a methodology of soft computing, simulates human learning and inferencing processes on the basis of fuzzy concepts. This paper presents a hardware implementation of ALM. ALM has processing engines called IDS, which are tasked with extracting useful
Results 1 - 10
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198,488
