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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 501–550 of 903 papers

TitleStatusHype
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis—0
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification—0
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification—0
Retrieval of surgical phase transitions using reinforcement learning—0
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method—0
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Robust Nonparametric Regression with Metric-Space Valued Output—0
Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem—0
Scalable Cross Validation Losses for Gaussian Process Models—0
Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning—0
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
SCREENER: A general framework for task-specific experiment design in quantitative MRI—0
SC-UPB at the VarDial 2019 Evaluation Campaign: Moldavian vs. Romanian Cross-Dialect Topic Identification—0
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection—0
Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms—0
Self-Training: A Survey—0
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes—0
Semantic Softmax Loss for Zero-Shot Learning—0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
Semi-supervised binary classification with latent distance learning—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Semi-supervised Outlier Detection using Generative And Adversary Framework—0
Semi-Supervised Zero-Shot Classification With Label Representation Learning—0
Sentence-wise Smooth Regularization for Sequence to Sequence Learning—0
Sentiment Analysis for YouTube Comments in Roman Urdu—0
Sequential Binary Classification for Intrusion Detection—0
Sequential Classification with Empirically Observed Statistics—0
Set-valued classification -- overview via a unified framework—0
Set-valued prediction in hierarchical classification with constrained representation complexity—0
Several Tunable GMM Kernels—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
Shallow Domain Adaptive Embeddings for Sentiment Analysis—0
Sheaf HyperNetworks for Personalized Federated Learning—0
SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games—0
Simpson's Bias in NLP Training—0
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells—0
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation—0
Single-Label Multi-Class Image Classification by Deep Logistic Regression—0
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML—0
Solar Active Region Magnetogram Image Dataset for Studies of Space Weather—0
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles—0
Solving for multi-class: a survey and synthesis—0
Solving for multi-class using orthogonal coding matrices—0
Sparse Output Coding for Large-Scale Visual Recognition—0
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain—0
SphereFace2: Binary Classification is All You Need for Deep Face Recognition—0
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning—0
SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text—0
SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models—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