Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Optimistic bounds for multi-output learning
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-t…
General ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Split Learning for collaborative deep learning in healthcare
Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden t…
Binary ClassificationDeep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Graph Convolutional Network Representation Learning
Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (…
Multi-Label ClassificationNode ClassificationRepresentation LearningClassifier Chains: A Review and Perspectives
The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves linking together off-the-shelf binary classifiers in a chain structure,…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningSimultaneous Identification of Tweet Purpose and Position
Tweet classification has attracted considerable attention recently. Most of the existing work on tweet classification focuses on topic classification, which classifies tweets into several predefined categories, and senti…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines
In a standard multi-output classification scenario, both features and labels of training data are partially observed. This challenging issue is widely witnessed due to sensor or database failures, crowd-sourcing and nois…
ClassificationGeneral ClassificationImputationMulti-Label Classification+2A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOn-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification
The goal of eXtreme Multi-label Learning (XML) is to automatically annotate a given data point with the most relevant subset of labels from an extremely large vocabulary of labels (e.g., a million labels). Lately, many a…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningCross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of d…
ClassificationGeneral ClassificationGraph Embeddingimage-classification+5Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds
Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the o…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning
Currently there are several well-known approaches to non-intrusive appliance load monitoring rule based, stochastic finite state machines, neural networks and sparse coding. Recently several studies have proposed a new a…
Deep LearningDictionary LearningGeneral ClassificationMeter Reading+2Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification
This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approach (developed for single label classific…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Multi-label Classification for Automatic Tag Prediction in the Context of Programming Challenges
One of the best ways for developers to test and improve their skills in a fun and challenging way are programming challenges, offered by a plethora of websites. For the inexperienced ones, some of the problems might appe…
Deep LearningGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semant…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Orderless Recurrent Models for Multi-label Classification
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. …
AttributeClassificationDecoderGeneral Classification+2An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy
Audio classification can distinguish different kinds of sounds, which is helpful for intelligent applications in daily life. However, it remains a challenging task since the sound events in an audio clip is probably mult…
Audio ClassificationClassificationGeneral ClassificationMulti-Label Classification+1Multi-Label Classification with Label Graph Superimposing
Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph conv…
AttributeClassificationGeneral ClassificationMulti-Label Classification+2Prototypical Networks for Multi-Label Learning
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningInterpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect sev…
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPose Guided Attention for Multi-label Fashion Image Classification
We propose a compact framework with guided attention for multi-label classification in the fashion domain. Our visual semantic attention model (VSAM) is supervised by automatic pose extraction creating a discriminative f…
ClassificationGeneral Classificationimage-classificationImage Classification+2