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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 801–810 of 903 papers

TitleStatusHype
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss—0
Distribution-Free Federated Learning with Conformal Predictions—0
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift—0
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation—0
Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
DPPMask: Masked Image Modeling with Determinantal Point Processes—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines—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
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified