Results 21 to 30 of about 4,472,392 (304)
Molecular & Cellular Proteomics, 2014 Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination
J. Cox +5 more
semanticscholar +1 more source
Bioinform., 2007 MOTIVATION Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are ...
M. Jakobsson, N. Rosenberg
semanticscholar +1 more source
A flexible mandatory access control policy for XML databases [PDF]
, 2007 A flexible mandatory access control policy (MAC) for XML databases is presented in this paper. The label type and label access policy can be defined according to the requirements of applications.
Jin, R, Lü, K, Zhu, H
core +1 more source
Colloids and Interfaces, 2019 Adsorption of hyperbranched arabinogalactan-proteins (AGPs) from two plant exudates, A. senegal and A. seyal, was thoroughly studied at the solid−liquid interface using quartz crystal microbalance with dissipation monitoring (QCM-D), surface ...
Athénaïs Davantès +4 more
doaj +1 more source
Large-Scale Multi-Label Learning with Incomplete Label Assignments [PDF]
, 2014 Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations.
Fan, Wei +6 more
core +2 more sources
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [PDF]
Knowledge Discovery and Data Mining, 2019 Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems.
Hongwei Wang +6 more
semanticscholar +1 more source
The Emerging Trends of Multi-Label Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data.
Weiwei Liu +3 more
semanticscholar +1 more source
Label Propagation for Deep Semi-Supervised Learning [PDF]
Computer Vision and Pattern Recognition, 2019 Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks.
Ahmet Iscen +3 more
semanticscholar +1 more source
Disentangling Label Distribution for Long-tailed Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2020 The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution.
Youngkyu Hong +5 more
semanticscholar +1 more source
Multi-label classification using ensembles of pruned sets [PDF]
, 2008 This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By
Holmes, Geoffrey +2 more
core +2 more sources