Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Neural Graph Machines: Learning Neural Networks Using Graphs
Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a train…
Document ClassificationGeneral Classificationintent-classificationIntent Classification+2On the benefits of output sparsity for multi-label classification
The multi-label classification framework, where each observation can be associated with a set of labels, has generated a tremendous amount of attention over recent years. The modern multi-label problems are typically lar…
ClassificationGeneral ClassificationGeneralization BoundsMulti-Label Classification+1Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input. Once the model is trained, we perform inference …
General ClassificationImage SegmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Multi-label image classification is a fundamental but challenging task in computer vision. Great progress has been achieved by exploiting semantic relations between labels in recent years. However, conventional approache…
ClassificationGeneral Classificationimage-classificationImage Classification+2Nearest Labelset Using Double Distances for Multi-label Classification
Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation betwe…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONIs a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?
We study the performance of data-driven, a priori and random approaches to label space partitioning for multi-label classification with a Gaussian Naive Bayes classifier. Experiments were performed on 12 benchmark data s…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA scikit-based Python environment for performing multi-label classification
scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal operations. It provides native Python i…
General ClassificationManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widel…
ClusteringInformation RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Hierarchical Partitioning of the Output Space in Multi-label Data
Hierarchy Of Multi-label classifiers (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and s…
ClusteringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningThe More You Know: Using Knowledge Graphs for Image Classification
One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can lear…
ClassificationGeneral Classificationimage-classificationImage Classification+3Chinese Grammatical Error Diagnosis Using Single Word Embedding
Abstract Automatic grammatical error detection for Chinese has been a big challenge for NLP researchers. Due to the formal and strict grammar rules in Chinese, it is hard for foreign students to master Chinese. A compute…
Grammatical Error DetectionLanguage ModelingLanguage ModellingMulti-Label ClassificationDomainless Adaptation by Constrained Decoding on a Schema Lattice
In many applications such as personal digital assistants, there is a constant need for new domains to increase the system{'}s coverage of user queries. A conventional approach is to learn a separate model every time a ne…
Multi-Label ClassificationSpoken Language UnderstandingCost-Sensitive Reference Pair Encoding for Multi-Label Learning
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors du…
Active LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningLearning Features of Music from Scratch
This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classic…
BIG-bench Machine LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMusic TranscriptionSemantic Regularisation for Recurrent Image Annotation
The "CNN-RNN" design pattern is increasingly widely applied in a variety of image annotation tasks including multi-label classification and captioning. Existing models use the weakly semantic CNN hidden layer or its tran…
General ClassificationImage CaptioningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep Label Distribution Learning with Label Ambiguity
Convolutional Neural Networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its success. However, it…
Age EstimationClassificationGeneral ClassificationHead Pose Estimation+4Structure-Aware Classification using Supervised Dictionary Learning
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold …
ClassificationDictionary LearningGeneral ClassificationMulti-Label Classification+1A Novel Progressive Multi-label Classifier for Classincremental Data
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added an…
class-incremental learningClass Incremental LearningGeneral ClassificationIncremental Learning+2INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-base…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Aspect Category DetectionAspect Extraction+5DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a la…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2