Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification
Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi…
ClassificationComputational EfficiencyEmotion ClassificationGeneral Classification+4Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR da…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning+3Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees
Here we study the problem of learning labels for large text corpora where each document can be assigned a variable number of labels. The problem is trivial when the label dimensionality is small and can be easily solved …
Learning TheoryMulti-Label LearningPAC learningSimilarity-based Multi-label Learning
Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-base…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Online Boosting Algorithms for Multi-label Ranking
We consider the multi-label ranking approach to multi-label learning. Boosting is a natural method for multi-label ranking as it aggregates weak predictions through majority votes, which can be directly used as scores to…
Multi-Label LearningDynamic classifier chains for multi-label learning
In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label or…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningDeep Determinantal Point Process for Large-Scale Multi-Label Classification
We study large-scale multi-label classification (MLC) on two recently released datasets: Youtube-8M and Open Images that contain millions of data instances and thousands of classes. The unprecedented problem scale poses …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Multi-Label Learning of Part Detectors for Heavily Occluded Pedestrian Detection
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by par…
Multi-Label LearningPedestrian DetectionLeveraging Distributional Semantics for Multi-Label Learning
We present a novel and scalable label embedding framework for large-scale multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using Distributional Semantics). Our approach draws inspiration from ideas rooted …
Document EmbeddingMissing LabelsMulti-Label LearningWord EmbeddingsSubset Labeled LDA for Large-Scale Multi-Label Classification
Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address multi-label learning tasks. Previous work has shown it to perform in par wit…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding
Human action recognition refers to automatic recognizing human actions from a video clip. In reality, there often exist multiple human actions in a video stream. Such a video stream is often weakly-annotated with a set o…
Action RecognitionMulti-Label LearningMulti-label zero-shot learningTemporal Action Localization+2Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and t…
Action RecognitionActivity RecognitionHuman Activity RecognitionMulti-Label Learning+1Adversarial Training for Relation Extraction
Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label le…
General ClassificationImage ClassificationMulti-Label LearningRelation+2Scalable Generative Models for Multi-label Learning with Missing Labels
We present a scalable, generative framework for multi-label learning with missing labels. Our framework consists of a latent factor model for the binary label matrix, which is coupled with an exposure model to accou…
Missing LabelsMulti-Label Learningparameter estimationFood Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet. We tackle the problem of food ingredients recognition as a multi-label learning problem. We propose a me…
Multi-Label ClassificationMulti-Label LearningDocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as rec…
ArticlesMulti-Label LearningTAGMulti-Label Learning with Label Enhancement
The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding labe…
Multi-Label LearningA Procedural Texture Generation Framework Based on Semantic Descriptions
Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic …
Multi-Label LearningTexture SynthesisDeep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets …
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing
Action Unit (AU) detection becomes essential for facial analysis. Many proposed approaches face challenging problems in dealing with the alignments of different face regions, in the effective fusion of temporal informati…
Action Unit DetectionMulti-Label Learning