Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Tsang, IW"'
Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::004dae014bc720081b0b4b9e8245d7c0
https://hdl.handle.net/10453/167392
https://hdl.handle.net/10453/167392
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract the aspect-related information. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30c8705387c20b6a00485b14b6c0025d
http://arxiv.org/abs/2106.10816
http://arxiv.org/abs/2106.10816
© 2019 Elsevier B.V. Most previous methods in heterogeneous transfer learning learn a cross-domain feature mapping between different domains based on some cross-domain instance-correspondences. Such instance-correspondences are assumed to be represe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::dd6cd8006b401144425c8a4f6c154f68
https://hdl.handle.net/10453/136809
https://hdl.handle.net/10453/136809
© 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One majo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::cd37abf787e95f7d868d6fbd5d63a055
https://hdl.handle.net/10453/136223
https://hdl.handle.net/10453/136223
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Due to the nonlinear but highly interpretable representations, decision tree (DT) models have significantly attracted a lot of attention of resea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::62ea489d0165b0d791e4b183ea9dd529
https://hdl.handle.net/10453/121806
https://hdl.handle.net/10453/121806
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label predi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::a8408b2f1ea0c231930acfad8d88a6bc
https://hdl.handle.net/10453/121779
https://hdl.handle.net/10453/121779
To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::826d37d46b2da0b76943fa70667d6637
https://hdl.handle.net/10453/120684
https://hdl.handle.net/10453/120684
We propose a two-phase framework to adapt existing relation extraction classifiers to extract relations for new target domains. We address two challenges: negative transfer when knowledge in source domains is used without considering the differences
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::a009b5ae3c3062571ff3242f83bc4fe7
https://hdl.handle.net/10453/121621
https://hdl.handle.net/10453/121621
Designing a classifier in the absence of labeled data is becoming a common encounter as the acquisition of informative labels is often difficult or expensive, particularly on new uncharted target domains. The feasibility of attaining a reliable class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::b8a7c7dda2676774a7e52389b98f6e33
https://hdl.handle.net/10453/29706
https://hdl.handle.net/10453/29706
Due to myriads of classes, designing accurate and efficient classifiers becomes very challenging for multi-class classification. Recent research has shown that class structure learning can greatly facilitate multi-class learning. In this paper, we pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08b9ab2c9bdc8e93eaeee2fef60234c1