SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 871–880 of 903 papers

TitleStatusHype
Max-Margin based Discriminative Feature Learning—0
Multi-Class Deep Boosting—0
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification—0
Target Fishing: A Single-Label or Multi-Label Problem?—0
Generalized Conditional Gradient for Sparse Estimation—0
Adaptive Multinomial Matrix Completion—0
Learning Deep Structured Models—0
Region-based Discriminative Feature Pooling for Scene Text Recognition—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified