Results 71 to 80 of about 637,915 (182)

Multi-Label Classifier Chains for Bird Sound [PDF]

, 2013
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species.
Briggs, Forrest, Fern, Xiaoli Z., Irvine, Jed   +2 more
core   +1 more source

OMAL: A Multi-Label Active Learning Approach from Data Streams

Entropy
With the rapid growth of digital computing, communication, and storage devices applied in various real-world scenarios, more and more data have been collected and stored to drive the development of machine learning techniques.
Qiao Fang, Chen Xiang, Jicong Duan, Benallal Soufiyan, Changbin Shao, Xibei Yang, Sen Xu, Hualong Yu   +7 more
doaj   +1 more source

Locally Non-linear Embeddings for Extreme Multi-label Learning [PDF]

, 2015
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set.
Bhatia, Kush, Jain, Himanshu, Jain, Prateek, Kar, Purushottam, Varma, Manik   +4 more
core  

Multi-Label Event-Prediction Model Based on Event-Evolution Graph [PDF]

Jisuanji gongcheng
Multilabel event prediction refers to the prediction of whether multiple associated events will occur in the future, which requires the simultaneous prediction of multiple target events and comparing it with the conventional single-label event prediction.
WANG Huazhen, XU Ze, SUN Yue, QIU Bin, CHEN Jian, QIU Qiangbin
doaj   +1 more source

MEKA: A multi-label/multi-target extension to WEKA [PDF]

, 2016
Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-
Holmes, Geoffrey, Pfahringer, Bernhard, Read, Jesse, Reutemann, Peter   +3 more
core   +1 more source

Food Ingredients Recognition Through Multi-label Learning [PDF]

, 2017
8 ...
Bolaños, Marc, Ferrà, Aina, Radeva, Petia   +2 more
openaire   +2 more sources

Multi‐instance multi‐label learning of natural scene images: via sparse coding and multi‐layer neural network

IET Computer Vision, 2018
The classification of natural scene images is multi‐instance multi‐label (MIML) for many labels that exist in a natural scene image. The traditional method of solving MIML is to degenerate it into single‐instance single‐label learning (SISL).
Hu Zhang, Wei Wu, Ding Wang
doaj   +1 more source

Adversarial Partial Multi-Label Learning

, 2019
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial ...
Yan, Yan, Guo, Yuhong
openaire   +2 more sources

Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities. [PDF]

Bioinformatics, 2022
Pang Y, Yao L, Xu J, Wang Z, Lee TY.
europepmc   +1 more source

DLKN-MLC: A Disease Prediction Model via Multi-Label Learning. [PDF]

Int J Environ Res Public Health, 2022
Li B, Zhang Y, Wu X.
europepmc   +1 more source