discriminatively


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dis·crim·i·na·tive

 (dĭ-skrĭm′ə-nā′tĭv, -nə-tĭv)
adj.
1. Drawing distinctions.
2. Marked by or showing prejudice: discriminative hiring practices.

dis·crim′i·na′tive·ly adv.
American Heritage® Dictionary of the English Language, Fifth Edition. Copyright © 2016 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved.
References in periodicals archive ?
Ramanan, "Object Detection with Discriminatively Trained Part-Based Models." Article (CrossRef Link)
Murino, "Person re-identification by discriminatively selecting parts and features," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol.
Sha, "Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation," in Proceedings of the 30th International Conference on Machine Learning, ICML 2013, pp.
In this study, SHP2 was confirmed to be discriminatively expressed in human thyroid tumour and normal tissues as shown by IHC, which allowed it to be a potential marker for the identification of thyroid tumour by molecular ultrasound imaging with higher diagnostic accuracy.
Ney, "Patch-based object recognition using discriminatively trained Gaussian mixtures," in Proceedings of BMVC 2006, pp.
Ramanan, "Object detection with discriminatively trained part-based models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.
The effective observation models are constructed based on the learned multi life span dictionary for both generatively and discriminatively. In addition, these are deployed into the Bayesian sequential estimation framework for tracking based on the particle filter implementation.
A discriminatively trained, multiscale, deformable part model.
Ramanan, "A discriminatively trained, multiscale, deformable part model," in 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.
To improve the performances, many improved naive Bayes algorithms are proposed in literature, such as the Averaged One-Dependence Estimators (AODE) [14], Weighted Average of One-Dependence Estimators (WAODE) [15], Hidden Naive Bayes (HNB) [16], Deep Feature Weighted Naive Bayes (DFWNB) [17], Discriminatively Weighted Naive Bayes (DWNB) [18], Averaged Tree Augmented Naive Bayes (ATAN) [19], and Super Parent TAN (SP) [14].
Thus, structurally, the SRI would measure diversified health impairments more discriminatively than the MRF-26, although it may take more time to be administered.