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 701–750 of 903 papers

TitleStatusHype
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—0
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net—0
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter—0
Multimodal Speech Emotion Recognition and Ambiguity ResolutionCode0
Looking back at Labels: A Class based Domain Adaptation TechniqueCode0
Deep Distribution RegressionCode0
A Bayesian Approach for Accurate Classification-Based Aggregates—0
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation—0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
It's Only Words And Words Are All I Have—0
Elimination of All Bad Local Minima in Deep Learning—0
Multi-class Classification without Multi-class LabelsCode0
Multi-Label Adversarial Perturbations—0
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
Sentence-wise Smooth Regularization for Sequence to Sequence Learning—0
A multi-class structured dictionary learning method using discriminant atom selection—0
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot LearningCode0
Multi-Class Learning: From Theory to Algorithm—0
Enhanced Network Embedding with Text InformationCode0
MEMOIR: Multi-class Extreme Classification with Inexact Margin—0
Single-Label Multi-Class Image Classification by Deep Logistic Regression—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Streaming Network Embedding through Local Actions—0
Theoretical Analysis of Adversarial Learning: A Minimax Approach—0
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification—0
Automated Multi-Label Classification based on ML-Plan—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
Machine Learning Methods for Track Classification in the AT-TPCCode0
Concise Explanations of Neural Networks using Adversarial TrainingCode0
Multi-class Classification Model Inspired by Quantum Detection Theory—0
Improved Generalization Bounds for Adversarially Robust Learning—0
Model Transfer with Explicit Knowledge of the Relation between Class Definitions—0
Phrase-level Self-Attention Networks for Universal Sentence Encoding—0
Unsupervised Adversarial Invariance—0
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference—0
Solving for multi-class: a survey and synthesis—0
Semi-Supervised Deep Learning with MemoryCode0
COFGA: Classification Of Fine-Grained Features In Aerial Images—0
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data—0
Punctuation as Native Language Interference—0
Filtering Aggression from the Multilingual Social Media Feed—0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
Deep attention-based classification network for robust depth predictionCode0
Identifying Domain Independent Update Intents in Task Based Dialogs—0
Dynamic Spectrum Matching with One-shot Learning—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data—0
Large scale classification in deep neural network with Label Mapping—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
#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified