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Found 6 papers, 1 papers with code
Probabilistic Label Trees for Extreme Multi-label Classification
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
On the computational complexity of the probabilistic label tree algorithms
First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type.
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly.
Online F-Measure Optimization
In this paper, we study the problem of F-measure maximization in the setting of online learning.
On the Bayes-optimality of F-measure maximizers
The F-measure, which has originally been introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction.
