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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/madbix/bnlearn-clone-3.4
공식 코드A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
https://github.com/mahyarbolhassan/CovidClassification
공식 코드https://github.com/mainaksingha01/odg-clip
공식 코드Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
https://github.com/majid9418/Breast-Tumor-Classification-Histopathological
공식 코드Breast Tumor Classification Using EfficientNet Deep Learning Model
https://github.com/malllabiisc/lcn
공식 코드https://github.com/marcofiorucci/ot-4-change-detection
공식 코드Optimal Transport for Change Detection on LiDAR Point Clouds
https://github.com/markopalangetic/fgac_experiments
공식 코드https://github.com/marsh312/dtr
공식 코드Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
https://github.com/maxc01/metacal
공식 코드https://github.com/mb7419/pendencyprediction
공식 코드Predicting delays in Indian lower courts using AutoML and Decision Forests
https://github.com/mhaft/octseg
공식 코드https://github.com/microsoft/geo-vision-labeler
공식 코드GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
https://github.com/mijoo308/energy-calibration
공식 코드Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset
https://github.com/miraiaroha/ACAN
공식 코드Attention-based Context Aggregation Network for Monocular Depth Estimation
https://github.com/ml-group-sdu/macro-auc-cl
공식 코드Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
https://github.com/mlmed/torchxrayvision
공식 코드TorchXRayVision: A library of chest X-ray datasets and models
https://github.com/mmaher22/instance-based-label-smoothing
공식 코드Instance-based Label Smoothing For Better Calibrated Classification Networks
https://github.com/ndb796/machineunlearning
공식 코드https://github.com/nlpAThits/hyfi
공식 코드A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification
https://github.com/nspunn1993/COVID-19-PA-CXR-fused-dataset
공식 코드https://github.com/nvtien457/nearbypatchcl
공식 코드https://github.com/nylesiddiqui/minecraft-mouse-dynamics-dataset
공식 코드Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication
https://github.com/omar-sharif03/CONSTRAINT-AAAI2021
공식 코드Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media
https://github.com/onelearn/onelearn
공식 코드https://github.com/optimization-ai/icml2023_ldr
공식 코드https://github.com/oracle/tribuo
공식 코드https://github.com/oscarllorente/positive_active_saliency_maps
공식 코드A matter of attitude: Focusing on positive and active gradients to boost saliency maps
https://github.com/osmanberke/Deep-SSVEP-BCI
공식 코드A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
https://github.com/pberjon/article-accent-recognition
공식 코드Analysis of French Phonetic Idiosyncrasies for Accent Recognition
https://github.com/pcwhy/xml-intrusiondetection
공식 코드Explainable AI for Comparative Analysis of Intrusion Detection Models
https://github.com/pfunami/CAMRI_Loss
공식 코드CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
https://github.com/pigejianghai/projects
공식 코드https://github.com/pkunlp-icler/aged
공식 코드Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling
https://github.com/pltrees/abcboost
공식 코드https://github.com/ppwangyc/rvt
공식 코드Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.