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 611–620 of 903 papers

TitleStatusHype
Learning across Data Owners with Joint Differential Privacy—0
Learning Deep Structured Models—0
Learning Disentangled Label Representations for Multi-label Classification—0
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions—0
Learning Kernels Using Local Rademacher Complexity—0
Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition—0
Learning Mutual Fund Categorization using Natural Language Processing—0
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs—0
Learning Optimal Fair Scoring Systems for Multi-Class Classification—0
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data—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