In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple target labels must be assigned to each instance. Multi-label classification should not be confused with multiclass classification, which is the problem of categorizing instances into one of more than two classes. Formally, multi-label learning can be phrased as the problem of finding a model that maps inputs x to binary vectors y, rather than scalar outputs as in the ordinary classification problem.
Multi-Label Classification Lecture Notes and Tutorials PDF
Multiclass and Multi-label Classification
Sep 25, 2018 — Beyond binary classification. • All classifiers we've looked at so far have predicted one of two classes. • We'll learn two main ways of predicting ...
Multi-Label Collective Classification
posed multi-label collective classification approach can effectively boost classification performances in multi- label relational datasets. 1 Introduction. Traditional ...by X Kong · Cited by 55 · Related articles
Empirical Studies on Multi-label Classification
1 Introduction. The multi-labeled classification problem is more diffi- cult than the traditional multi-class classification problem. (which usually refers to simply ...by T Li · Cited by 59 · Related articles
Crowdsourcing Multi-Label Classification for Taxonomy Creation
Abstract. Recent work has introduced CASCADE, an algorithm for creating a ... multi-label classification optimizes CASCADE's most costly step (categorization) ...by JBMDS Weld · 2013 · Related articles
A Generative Probabilistic Model for Multi-label Classification
In our evaluations, the proposed model achieved promising results on various data sets. 1. Introduction. 1.1 Multi-label classification. In the traditional definition ...by H Wang · Cited by 48 · Related articles
Large Scale Max-Margin Multi-Label Classification with Priors
Introduction. The objective in multi-label classification is to predict a set of relevant binary labels for a given input. A key aspect is dealing with the exponentially ...by B Hariharan · Cited by 164 · Related articles
Efficient Multi-label Ranking for Multi-class Learning
They are often cast into multi-label learning, in which each object can be simultaneously classified into more than one class. The most widely used approaches di-.by SS Bucak · Cited by 53 · Related articles
Neural Kernels and ImageNet Multi-Label Accuracy
May 29, 2020 — Recht for additional research guidance. ... on ImageNet with a multi-label accuracy metric in order to better understand the robustness of.
Large-Scale Multi-label Ensemble Learning on Spark
In multi-label classification the instances ... Multi-label classification has attracted growing interest in ... data from the distributed file system, which introduces a.by J Gonzalez-Lopez · Cited by 8 · Related articles
Large-scale Multi-label Learning with Missing Labels
Note that although we focus mostly on the binary classification setting in this paper, our methods easily ex- tend to the multi-class setting where y j i ∈ {1, 2,...,C}.by HF Yu · Cited by 376 · Related articles
Multi-Target Classification and Regression in Wineinformatics
and multiclass classification and regression schemes are applied to price and grade, ... [Online]. Available: J Palmer · 2018 · Cited by 1 · Related articles
Multi-Class Active Learning for Image Classification
active learning algorithms for multi-class problems. The principal idea in active ... consider a Support Vector Machine trained on some training examples.by AJ Joshi · Cited by 410 · Related articles
Boosting and Ensembles; Multi-class Classification and Ranking
learners. • It is a member of a family of Ensemble Algorithms, but has stronger ... A new set of written notes will accompany most lectures, with some more details ...
Foundations of Machine Learning Multi-Class Classification
Notes. In most tasks considered, number of classes. For large, problem often not treated as a multi- class classification problem (ranking or density estimation ...
CSC321 Tutorial 4: Multi-Class Classification with PyTorch
Introduce the MNIST dataset, which contains 28x28 pixel images of hand-written digits. • Introduce how to use of PyTorch to build and train models. • (If we have ...
Multi-Task Multi-Sample Learning
We introduce here multi-sample learning (MSL), for jointly learning multiple. E-SVMs. This has the flexibility to travel between the two ends of the learning spectrum ...by Y Aytar · Related articles
Label Propagation in RGB-D Video
[7] introduced a filtering algorithm that predicts per-pixel label distribution from a separate model in the current frame, then it temporally smooths out the prediction.by MA Reza · Cited by 5 · Related articles
Active Frame Selection for Label Propagation in Videos
frames k should be labeled in order to minimize the total manual effort spent labeling and correcting propagation errors. We demonstrate our method's clear.by S Vijayanarasimhan · Cited by 114 · Related articles
Balanced Label Propagation for Partitioning Massive Graphs
The algorithm we present uses label propagation to relocate inefficiently assigned nodes while respecting strict shard balancing con- straints. We show how this ...by J Ugander · 2013 · Cited by 183 · Related articles
Label Propagation from ImageNet to 3D Point Clouds
Such mas- sive point cloud data has shown great potential for solv- ing several ... each pi a semantic label l from an exclusive label set L, as propagated from the ...by Y Wang · Cited by 33 · Related articles
Higher-Order Label Homogeneity and Spreading in Graphs
Let us elaborate this using a small friendship network example, shown in Figure 1. ... (ii) Algorithm: We develop Higher-Order Label Spreading (HOLS) to leverage ... graph SSL techniques are label propagation [40] and label spread- ing [39].by D Eswaran · Cited by 3 · Related articles
Global Linear Neighborhoods for Efficient Label Propagation
In this example, the algorithm proposed by [18] is applied to classify data points from two moon clusters shown in. Fig. 1(a). When k is too small, several ...by Z Tian · Cited by 29 · Related articles
Linear Classification 1 Review of Classification
In these notes we discuss parametric classification, in particular, linear ... are linear functions of the covariate X. For K = 2, we have a binary classification ...
Multi-layer Perceptron 1 The Multi-layer Perceptron
Matlab tutorials for neural network design: nnd9sd % Steepest descent ... Multi-layer Perceptron: Barnabas Poczos ... 2 The back-propagation algorithm. 2.1 The ...
Multi-Task Feature Learning
Our algorithm can also be used, as a special case, to simply select – not learn – a few common features across the tasks. 1 Introduction. Learning multiple related ...by A Argyriou · Cited by 1418 · Related articles
