Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
We propose a unified framework for multi-view subspace learning to learn individual orthogonal projections for all views. The framework integrates the correlations within multiple views, supervised discriminant capacity,…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Large Multi-Target Dataset of Common Bengali Handwritten Graphemes
Latin has historically led the state-of-the-art in handwritten optical character recognition (OCR) research. Adapting existing systems from Latin to alpha-syllabary languages is particularly challenging due to a sharp co…
Multi-Label ClassificationOptical Character RecognitionOptical Character Recognition (OCR)Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph
The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the…
Disease PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAsymmetric Loss For Multi-Label Classification
In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, and can lead to under-emphasizing gradient…
ClassificationGeneral ClassificationImage ClassificationMulti-Label Classification+3Learning from eXtreme Bandit Feedback
We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions a…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsProbabilistic Label Trees for Extreme Multi-label Classification
Extreme multi-label classification (XMLC) is a learning task of tagging instances with a small subset of relevant labels chosen from an extremely large pool of possible labels. Problems of this scale can be efficiently h…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1On Data Augmentation for Extreme Multi-label Classification
In this paper, we focus on data augmentation for the extreme multi-label classification (XMC) problem. One of the most challenging issues of XMC is the long tail label distribution where even strong models suffer from in…
ClassificationData AugmentationExtreme Multi-Label ClassificationGeneral Classification+2Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress recently mostly through employing reinforcement learning methods. However, these approaches have become very sophisticated. It is tim…
DecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONreinforcement-learning+3Compact 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+2Convex Calibrated Surrogates for the Multi-Label F-Measure
The F-measure is a widely used performance measure for multi-label classification, where multiple labels can be active in an instance simultaneously (e.g. in image tagging, multiple tags can be active in any image). In p…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNeural Networks Enhancement with Logical Knowledge
In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i.e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds. In a previous …
Multi-Label ClassificationDeep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
CXRs are a crucial and extraordinarily common diagnostic tool, leading to heavy research for CAD solutions. However, both high classification accuracy and meaningful model predictions that respect and incorporate clinica…
DiagnosticGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemi-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+2Imbalanced Continual Learning with Partitioning Reservoir Sampling
Continual learning from a sequential stream of data is a crucial challenge for machine learning research. Most studies have been conducted on this topic under the single-label classification setting along with an assumpt…
Continual LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSOLAR: Sparse Orthogonal Learned and Random Embeddings
Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents. …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONItem Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions. A lot of research has been done for either multi-labe…
Graph Neural NetworkInformation RetrievalLink PredictionMulti-Label Classification+4EmoGraph: Capturing Emotion Correlations using Graph Networks
Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnections among them. In this paper, we explore …
ClassificationEmotion ClassificationEmotion RecognitionGeneral Classification+2Instance-Aware Graph Convolutional Network for Multi-Label Classification
Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task by introducing label dependencies based on statistical label co-occurrence of data. However, in previous methods, la…
ClassificationGeneral ClassificationMulti-Label 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+2SPL-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 Learning