Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Learning by Minimizing the Sum of Ranked Range
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual losses of a learning model over each trai…
Binary ClassificationGeneral ClassificationMulti-class ClassificationMulti-Label LearningCompact Learning for Multi-Label Classification
Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC encourages a popular framework named label …
ClassificationDimensionality ReductionGeneral ClassificationMulti-Label Classification+2Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to …
Active LearningDiagnosticGeneral ClassificationMulti-Label Classification+2Positive semidefinite support vector regression metric learning
Most existing metric learning methods focus on learning a similarity or distance measure relying on similar and dissimilar relations between sample pairs. However, pairs of samples cannot be simply identified as similar …
General ClassificationMetric LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets
Facial action units allow an objective, standardized description of facial micro movements which can be used to describe emotions in human faces. Annotating data for action units is an expensive and time-consuming task, …
Action Unit DetectionMissing LabelsMissing ValuesMulti-Label LearningSPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning
Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels. Furthermore, modeling the relationships between input and some (dull) …
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningRAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations
Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generally six prototypical facial expressions), …
Facial Expression RecognitionFacial Expression Recognition (FER)Multi-Label LearningOpen-Ended Question AnsweringLatent Topic-aware Multi-Label Classification
In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Bidirectional Loss Function for Label Enhancement and Distribution Learning
Label distribution learning (LDL) is an interpretable and general learning paradigm that has been applied in many real-world applications. In contrast to the simple logical vector in single-label learning (SLL) and multi…
Multi-Label LearningRecovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning
Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To addr…
Multi-Label LearningvalidMulti-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-l…
DiagnosticMulti-Label LearningMulti-Label Sampling based on Local Label Imbalance
Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before trai…
Multi-Label LearningA Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning
In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multip…
Missing LabelsModel SelectionMulti-Label LearningMulti-label Stream Classification with Self-Organizing Maps
Several learning algorithms have been proposed for offline multi-label classification. However, applications in areas such as traffic monitoring, social networks, and sensors produce data continuously, the so called data…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Incorporating Multiple Cluster Centers for Multi-Label Learning
Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of multi-label learning by exploiting label…
ClusteringData AugmentationMulti-Label LearningMLPSVM:A new parallel support vector machine to multi-label learning
Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance metho…
Multi-Label LearningExpand Globally, Shrink Locally: Discriminant Multi-label Learning with Missing Labels
In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. However, these methods just utilize global …
Missing LabelsMulti-Label LearningPartial Multi-label Learning with Noisy Label Identification
Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML problems …
Multi-Label LearningMulti-label learning for dynamic model type recommendation
Dynamic selection techniques aim at selecting the local experts around each test sample in particular for performing its classification. While generating the classifier on a local scope may make it easier for singling ou…
modelMulti-Label LearningRecommendation SystemsVocal Bursts Type PredictionPartial Multi-label Learning with Label and Feature Collaboration
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML problem is practical in real-world scenar…
Multi-Label Learning