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Multi-class Classification
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/project-agml/agml
공식 코드https://github.com/ptomaszewska/limes
공식 코드Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift
https://github.com/quilograma/conformalpredictiontutorial
공식 코드Inductive Conformal Prediction: A Straightforward Introduction with Examples in Python
https://github.com/quwenjie/multiguard
공식 코드MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
https://github.com/rajiv256/NeuralCRNGen
공식 코드Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks
https://github.com/raoulheese/casimac
공식 코드https://github.com/renzhenwang/label-hierarchy-transition
공식 코드Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
https://github.com/rezabonyadi/LinearOEC
공식 코드https://github.com/rodsveiga/mc_perceptron
공식 코드Learning curves for the multi-class teacher-student perceptron
https://github.com/rolan2kn/tdabc-4-adac
공식 코드https://github.com/rsinghlab/maddi
공식 코드Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis
https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT
공식 코드SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT
https://github.com/saeed-anwar/PLCNN
공식 코드Deep localization of protein structures in fluorescence microscopy images
https://github.com/salu133445/arranger
공식 코드Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music
https://github.com/salusanga/alm-dnn
공식 코드Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
https://github.com/satyadevntv/ROT
공식 코드Efficient Robust Optimal Transport with Application to Multi-Label Classification
https://github.com/sb1990gtr/Semantic-Interactive-ML
공식 코드https://github.com/scikit-learn-contrib/mapie
공식 코드MAPIE: an open-source library for distribution-free uncertainty quantification
https://github.com/shinyoung3/mof_qnlp
공식 코드Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing
https://github.com/skmalviya/ai-gen_text_defactify
공식 코드SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection
https://github.com/slarabi/Gated-Perceptron
공식 코드https://github.com/snarles/ClassEx
공식 코드Extrapolating Expected Accuracies for Large Multi-Class Problems
https://github.com/snowood1/wenn
공식 코드Multidimensional Uncertainty-Aware Evidential Neural Networks
https://github.com/sonlam1102/vispamdetection
공식 코드https://github.com/srebuffi/semisup_scarse
공식 코드https://github.com/suzgunmirac/hupd
공식 코드https://github.com/talreiss/PANDA
공식 코드PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
https://github.com/tang-yuting/u-prr
공식 코드Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization
https://github.com/tianqi-py/mlgnc
공식 코드https://github.com/tjvsonsbeek/open-ended-medical-vqa
공식 코드Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models
https://github.com/tommyippoz/sprout
공식 코드Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers
https://github.com/tonmoy-hossain/mgaug
공식 코드MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations
https://github.com/tsinghuamedgao20/tabkanet
공식 코드TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
https://github.com/uchidalab/softmaxgradient-lrp
공식 코드Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation
https://github.com/uclanlp/ILPLearning
공식 코드An Integer Linear Programming Framework for Mining Constraints from Data
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