Results 311 to 320 of about 11,061,910 (343)
Some of the next articles are maybe not open access.
IEEE Transactions on Circuits and Systems for Video Technology, 2020
A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains.
Zhihao Peng 0002 +5 more
openaire +1 more source
A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains.
Zhihao Peng 0002 +5 more
openaire +1 more source
Proceedings of the 7th international conference on Intelligent user interfaces - IUI '02, 2002
The Active Learning Framework (ALF) creates a technologically and educationally sophisticated learning environment in which nurse practitioners, medical students and other health professional students can participate in virtual patient encounters anytime, anywhere. Learners are presented with web-based case studies and engage in open-ended, interactive
Russell Maulitz, Debra McGrath
openaire +1 more source
The Active Learning Framework (ALF) creates a technologically and educationally sophisticated learning environment in which nurse practitioners, medical students and other health professional students can participate in virtual patient encounters anytime, anywhere. Learners are presented with web-based case studies and engage in open-ended, interactive
Russell Maulitz, Debra McGrath
openaire +1 more source
Neurocomputing, 2014
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose.
Cheng Li +2 more
openaire +1 more source
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose.
Cheng Li +2 more
openaire +1 more source
2017 IEEE International Conference on Big Data (Big Data), 2017
Active learning is a common strategy to deal with large-scale data with limited labeling effort. In each iteration of active learning, a query is ready for oracle to answer such as what the label is for a given unlabeled data. Given the method, we can request the labels only for those data that are essential and save the labeling effort from oracle. We
Er-Chen Huang +2 more
openaire +1 more source
Active learning is a common strategy to deal with large-scale data with limited labeling effort. In each iteration of active learning, a query is ready for oracle to answer such as what the label is for a given unlabeled data. Given the method, we can request the labels only for those data that are essential and save the labeling effort from oracle. We
Er-Chen Huang +2 more
openaire +1 more source
Learning to Label with Active Learning and Reinforcement Learning
2021Training data labelling is financially expensive in domain-specific learning applications, which heavily relies on the intelligence from domain experts. Thus, with budget constraint, it is important to judiciously select high-quality training data for labelling in order to prevent over-fitting.
Xiu Tang +4 more
openaire +1 more source
Active learning with confidence
Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies Short Papers - HLT '08, 2008Active learning is a machine learning approach to achieving high-accuracy with a small amount of labels by letting the learning algorithm choose instances to be labeled. Most of previous approaches based on discriminative learning use the margin for choosing instances.
Mark Dredze, Koby Crammer
openaire +2 more sources
Educational Gerontology, 2006
Learning is an important aspect of aging productively. This paper describes results from 2645 respondents (aged from 50 to 74 + years) to a 165-variable postal survey in Australia. The focus is on learning and its relation to work; social, spiritual, and emotional status; health; vision; home; life events; and demographic details.
Boulton-Lewis, Gillian +2 more
openaire +4 more sources
Learning is an important aspect of aging productively. This paper describes results from 2645 respondents (aged from 50 to 74 + years) to a 165-variable postal survey in Australia. The focus is on learning and its relation to work; social, spiritual, and emotional status; health; vision; home; life events; and demographic details.
Boulton-Lewis, Gillian +2 more
openaire +4 more sources
Learning Activities and Activity Systems
2019This chapter considers what general principles can be developed from theories of how people learn and how these can be applied to learning with digital technologies in particular. Learning activity is offered as a helpful focus for educational design, and Activity Theory is used to develop a model of design practice which focuses on different aspects ...
openaire +1 more source
1995
The paper distinguishes between two different modes of learning by neural networks. Traditional networks learn in the passive mode by incorporating in their internal structure the regularities present in the input and teaching input they passively receive from outside.
Domenico Parisi, Federico Cecconi
openaire +1 more source
The paper distinguishes between two different modes of learning by neural networks. Traditional networks learn in the passive mode by incorporating in their internal structure the regularities present in the input and teaching input they passively receive from outside.
Domenico Parisi, Federico Cecconi
openaire +1 more source