https://bitbucket.org/params/dsmlr
공식 코드DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Papers with Code 아카이브
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/AI-Growth-Lab/Patent-Classification
공식 코드https://github.com/ATTPC/event-classification
공식 코드Machine Learning Methods for Track Classification in the AT-TPC
https://github.com/AbdelkaderMH/DomURLs_BERT
공식 코드https://github.com/AlonZolfi/YolOOD
공식 코드YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus
공식 코드KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
https://github.com/Arko98/Hostility-Detection-in-Hindi-Constraint-2021
공식 코드Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings
https://github.com/CquptZA/Uncertainty_Batch
공식 코드Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
https://github.com/DEFI-COLaF/LanguageMining
공식 코드KréyoLID From Language Identification Towards Language Mining
https://github.com/Dichoto/LGL-INR
공식 코드On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
https://github.com/EpistasisLab/pmlb
공식 코드PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
https://github.com/EpistasisLab/pmlb-manuscript
공식 코드PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
https://github.com/EpistasisLab/pmlbr
공식 코드PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
https://github.com/GT-RIPL/L2C
공식 코드https://github.com/GuangzhiMa/MCIMO
공식 코드Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
https://github.com/Hengwei-Zhao96/HOneCls
공식 코드One-Class Risk Estimation for One-Class Hyperspectral Image Classification
https://github.com/Hezi-Resheff/paper-log-bilinear-loss
공식 코드Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear Loss
https://github.com/Hope-Rita/EDGPAT
공식 코드Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
https://github.com/IdanAchituve/GP-Tree
공식 코드GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
https://github.com/IdePHICS/GaussMixtureProject
공식 코드Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
https://github.com/IsaacChanghau/DeepNaryECOC
공식 코드https://github.com/IssamLaradji/sls
공식 코드Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
https://github.com/JBall1/Food101-CNN
공식 코드Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment Analysis
https://github.com/JingruG/GATHER
공식 코드Open-Set Knowledge-Based Visual Question Answering with Inference Paths
https://github.com/JonathanWenger/pycalib
공식 코드https://github.com/LIBOL/SOL
공식 코드https://github.com/LingxiaoShawn/PG-Learn
공식 코드A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification
https://github.com/LiuHaiTao01/GPCnoise
공식 코드Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
https://github.com/LorenzoBini4/FlowCyt-Classification-Benchmark
공식 코드https://github.com/LorenzoBini4/GNN-HemaGraph
공식 코드HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph Attention
https://github.com/MReza89/Emulating-Spatio-Temporal-Realizations-of-Three-Dimensional-Isotropic-Turbulence-via-Deep-Sequence
공식 코드https://github.com/MZMMSEC/SO-DFD
공식 코드Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
https://github.com/Media-Smart/vedastr
공식 코드Revisiting Classification Perspective on Scene Text Recognition
https://github.com/NICTA/serene-benchmark
공식 코드https://github.com/Rennes-LiDAR-Platform/lidar_platform
공식 코드https://github.com/SMehrkanoon/Deep-brain-state-classification-of-MEG-data
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.