Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Student Performance Prediction with Optimum Multilabel Ensemble Model
One of the important measures of quality of education is the performance of students in the academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insig…
General ClassificationmodelMulti-class ClassificationMulti-Label Classification+3Intra-Camera Supervised Person Re-Identification: A New Benchmark
Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalabili…
Multi-Label LearningPerson Re-IdentificationBayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order. Each classifier in the sequence is responsible for predicting the relevance of one lab…
Multi-Label LearningDeep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction
Knowledge base provides a potential way to improve the intelligence of information retrieval (IR) systems, for that knowledge base has numerous relations between entities which can help the IR systems to conduct inferenc…
Information RetrievalMulti-Label LearningRelationRelation Extraction+1Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node features and graph topologic information to build learning models. However, as …
ClassificationGeneral ClassificationGraph ClassificationGraph Embedding+4Towards Interpretable Deep Extreme Multi-label Learning
Many Machine Learning algorithms, such as deep neural networks, have long been criticized for being "black-boxes"-a kind of models unable to provide how it arrive at a decision without further efforts to interpret. This …
BIG-bench Machine LearningMulti-Label LearningAtlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning
In recent years, computer vision techniques have made large advances in image recognition and been applied to aid radiological diagnosis. Computational pathology aims to develop similar tools for aiding pathologists in d…
DiagnosticMulti-Label LearningA Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems
Extreme classification is a classification task on an extremely large number of labels (tags). User generated labels for any type of online data can be sparing per individual user but intractably large among all users. I…
General ClassificationMulti-Label LearningOn a scalable problem transformation method for multi-label learning
Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label. A common challenge with this in real-world applications is that the label space…
Binary ClassificationGeneral ClassificationMulti-Label LearningRecommendation SystemsMulti-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only par…
Multi-Label LearningVariational Autoencoders for Sparse and Overdispersed Discrete Data
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data. Although probabilistic…
Collaborative FilteringMulti-Label LearningRecommendation SystemsSynthetic Oversampling of Multi-Label Data based on Local Label Distribution
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techn…
Multi-Label LearningBonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generali…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagno…
Active LearningMulti-Label LearningDeep Topic Models for Multi-label Learning
We present a probabilistic framework for multi-label learning based on a deep generative model for the binary label vector associated with each observation. Our generative model learns deep multi-layer latent embeddings …
Multi-Label LearningTopic ModelsComponent-Wise Boosting of Targets for Multi-Output Prediction
Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label lea…
Multi-Label LearningPredictionregressionWeakly Supervised Person Re-Identification
In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual; this labeling across multiple nonoverlapping camera views from raw video …
Multi-Label LearningPerson Re-IdentificationSemantic Bilinear Pooling for Fine-Grained Recognition
Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most o…
General ClassificationMulti-Label LearningA Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, w…
Meta-LearningMulti-Label LearningPseudo LabelCollaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the rea…
Multi-Label Learning