Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages
Recommending phrases from web pages for advertisers to bid on against search engine
queries is an important research problem with direct commercial impact. Most approaches …
queries is an important research problem with direct commercial impact. Most approaches …
Label embedding trees for large multi-class tasks
Multi-class classification becomes challenging at test time when the number of classes is
very large and testing against every possible class can become computationally infeasible …
very large and testing against every possible class can become computationally infeasible …
Fast and balanced: Efficient label tree learning for large scale object recognition
We present a novel approach to efficiently learn a label tree for large scale classification with
many classes. The key contribution of the approach is a technique to simultaneously …
many classes. The key contribution of the approach is a technique to simultaneously …
A 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 …
subset of relevant labels chosen from an extremely large pool of possible labels. Large label …
Extreme f-measure maximization using sparse probability estimates
K Jasinska, K Dembczynski… - International …, 2016 - proceedings.mlr.press
We consider the problem of (macro) F-measure maximization in the context of extreme multi-
label classification (XMLC), ie, multi-label classification with extremely large label spaces …
label classification (XMLC), ie, multi-label classification with extremely large label spaces …
An easy-to-hard learning paradigm for multiple classes and multiple labels
Many applications, such as human action recognition and object detection, can be
formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most …
formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most …
Model-powered conditional independence test
We consider the problem of non-parametric Conditional Independence testing (CI testing)
for continuous random variables. Given iid samples from the joint distribution $ f (x, y, z) $ of …
for continuous random variables. Given iid samples from the joint distribution $ f (x, y, z) $ of …
On missing labels, long-tails and propensities in extreme multi-label classification
The propensity model introduced by Jain et al has become a standard approach for dealing
with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper …
with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper …
[HTML][HTML] Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: a large-scale benchmarking study
T Mortier, AD Wieme, P Vandamme… - Computational and …, 2021 - Elsevier
Today machine learning methods are commonly deployed for bacterial species identification
using MALDI-TOF mass spectrometry data. However, most of the studies reported in …
using MALDI-TOF mass spectrometry data. However, most of the studies reported in …
[PDF][PDF] Classifier cascades and trees for minimizing feature evaluation cost
Abstract Machine learning algorithms have successfully entered industry through many real-
world applications (eg, search engines and product recommendations). In these …
world applications (eg, search engines and product recommendations). In these …