Multi-Label Classification Lecture Notes and Tutorials PDF Download

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

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 ...
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Multi-Label Collective Classification

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
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Empirical Studies on Multi-label Classification

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
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Crowdsourcing Multi-Label Classification for Taxonomy Creation

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
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A Generative Probabilistic Model for Multi-label Classification

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
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Large Scale Max-Margin Multi-Label Classification with Priors

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
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Efficient Multi-label Ranking for Multi-class Learning

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
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Neural Kernels and ImageNet Multi-Label Accuracy

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.
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Large-Scale Multi-label Ensemble Learning on Spark

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
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Large-scale Multi-label Learning with Missing Labels

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
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Multi-Target Classification and Regression in Wineinformatics

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
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Multi-Class Active Learning for Image Classification

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
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Boosting and Ensembles; Multi-class Classification and Ranking

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 ...
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Foundations of Machine Learning Multi-Class Classification

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 ...
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CSC321 Tutorial 4: Multi-Class Classification with PyTorch

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 ...
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Multi-Task Multi-Sample Learning

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
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Label Propagation in RGB-D Video

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
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Active Frame Selection for Label Propagation in Videos

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
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Balanced Label Propagation for Partitioning Massive Graphs

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
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Label Propagation from ImageNet to 3D Point Clouds

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
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Higher-Order Label Homogeneity and Spreading in Graphs

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
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Global Linear Neighborhoods for Efficient Label Propagation

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
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Linear Classification 1 Review of Classification

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 ...
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Multi-layer Perceptron 1 The Multi-layer Perceptron

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 ...
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Multi-Task Feature Learning

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
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