Papers Extreme Multi-Label Classification
“Extreme Multi-Label Classification” 태그가 달린 논문 75편 · 필터 해제
Ranking-Based Autoencoder for Extreme Multi-label Classification
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers…
ClassificationExtreme Multi-Label ClassificationFeature ImportanceGeneral Classification+2SeCSeq: Semantic Coding for Sequence-to-Sequence based Extreme Multi-label Classification
Extreme multi-label classification (XMC) aims at assigning to an instance the most relevant subset of labels from a colossal label set. There has been some success in formulating the multi-label problem as sequence-to-se…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBlock-wise Partitioning for Extreme Multi-label Classification
Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions embed the label matrix to a low-dimensi…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1A no-regret generalization of hierarchical softmax to extreme multi-label classification
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled b…
Extreme Multi-Label ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONHFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages…
Extreme Multi-Label ClassificationMulti-Label ClassificationText CategorizationContent Explorer: Recommending Novel Entities for a Document Writer
Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further resea…
Extreme Multi-Label ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representatio…
Extreme Multi-Label ClassificationGaussian ProcessesGeneral ClassificationMulti-Label Classification+1Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we present a new method to learn linear …
compressed sensingDecoderExtreme Multi-Label ClassificationMulti-Label Classification+3Adversarial Extreme Multi-label Classification
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification e…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
Machine learning has played an important role in information retrieval (IR) in recent times. In search engines, for example, query keywords are accepted and documents are returned in order of relevance to the given query…
BIG-bench Machine LearningExtreme Multi-Label ClassificationGeneral ClassificationInformation Retrieval+3Subset Labeled LDA for Large-Scale Multi-Label Classification
Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address multi-label learning tasks. Previous work has shown it to perform in par wit…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets …
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2DiSMEC - 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+2Sparse Local Embeddings for Extreme Multi-label Classification
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches m…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+3Locally Non-linear Embeddings for Extreme Multi-label Learning
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches m…
Extreme Multi-Label ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2