2022/10/08 · To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning.

2022/10/08 · Extreme Multi-label Learning (XML) aims to annotate ob- jects with the relevant labels from an extremely large number of candidate labels. XML ...

2022/10/21 · A Survey on Extreme Multi-label Learning [72.9] マルチラベル学習は、近年、学術分野と産業分野の両方から大きな注目を集めている。

Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several ...

This paper formulates the extreme classification problem when predictions need to be made on training points with partially revealed labels.

k has large bias, as it leverages the labels of objects distant from the query. For solving these issues, we employ multiscale k-NN, which reduces the bias ...

In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph ...

In this paper, we review various approaches such as Embedding, Tree and One-vs-All methods to handle Extreme Multi-Label classification problems and have ...

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the ...

This paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical low-rank principle ...