Supervised Classification

Supervised classification aims to train models that accurately categorize data into predefined classes based on labeled examples. Current research emphasizes improving model robustness and fairness, particularly by addressing issues like imbalanced datasets and concept drift, often employing techniques such as generative adversarial networks for data augmentation and adaptive minimax risk classifiers for handling evolving data distributions. These advancements are crucial for enhancing the reliability and applicability of supervised classification across diverse fields, from medical diagnosis and anomaly detection in manufacturing to more accurate and ethical image recognition systems.

Papers

October 5, 2024

The OCON model: an old but gold solution for distributable supervised classification
Stefano Giacomelli, Marco Giordano, Claudia Rinaldi
Classification Code Striking GOld Supervised Classification One Class

June 7, 2024

Error Bounds of Supervised Classification from Information-Theoretic Perspective
Binchuan Qi
Deep Learning Deep Neural Network Generalization Error Information Theoretic Error Bound Generalization Capability Supervised Classification

May 28, 2024

Context-Specific Refinements of Bayesian Network Classifiers
Manuele Leonelli, Gherardo Varando
Bayesian Network Bayes Classifier Tree Model Supervised Classification Generative Classifier

May 14, 2024

Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction
Anri Patron, Ayush Prasad, Hoang Phuc Hau Luu, Kai Puolamäki
Supervised Learning Dimensionality Reduction Natural Gradient Area MaPPing Feature Extraction Weak Learner Supervised Classification Supervised Learning Method

September 8, 2023

Probabilistic Safety Regions Via Finite Families of Scalable Classifiers
Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene, Maurizio Mongelli
Machine Learning Scalable Learning Supervised Classification Safety Specification

July 28, 2023

Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach
Marco Russo, Edoardo Giusto, Bartolomeo Montrucchio
Quantum Computer Quantum Kernel Supervised Classification Quantum Kernel Estimation Neutral Atom Gate Function

June 8, 2023

A brief review of contrastive learning applied to astrophysics
Marc Huertas-Company, Regina Sarmiento, Johan Knapen
Contrastive Learning Self Supervised Astronomical Data High Dimensional Space Brief Review Supervised Classification

May 2, 2023

On the Impact of Data Quality on Image Classification Fairness
Aki Barry, Lei Han, Gianluca Demartini
Training Data Global Impact Data Quality Fairness Metric Image Classification Datasets Supervised Classification

March 3, 2023

Utilizing synthetic training data for the supervised classification of rat ultrasonic vocalizations
K. Jack Scott, Lucinda J. Speers, David K. Bilkey
Convolutional Neural Network Synthetic Training Data Ultrasonic Vocalization Supervised Classification Rat Image GMM CNN

November 24, 2022

Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification
Bosheng Yan, Chang-Tsun Li, Xuequan Lu
Deep Learning Deepfake Detection Low Quality Deepfake Dual Branch Supervised Classification Unsupervised Joint

October 28, 2022

Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network
Jihoon Chung, Bo Shen, Zhenyu, Kong
Generative Adversarial Network Anomaly Detection Balanced Dataset Additive Manufacturing Class Imbalanced Data Supervised Classification Imbalanced Training

October 6, 2022

Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Chenxi Sun, Hongyan Li, Moxian Song, Derun Cai, Baofeng Zhang, Shenda Hong
Deep Neural Network Prognosis Prediction Prognostic Model Sepsis Prediction Supervised Classification Consistency Based Diagnosis

August 14, 2022

Confidence-Guided Learning Process for Continuous Classification of Time Series
Chenxi Sun, Moxian Song, Derun Can, Baofeng Zhang, Shenda Hong, Hongyan Li
Time Series Learning Process Supervised Classification Multiple Distribution

June 2, 2022

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron Courville, Alessandro Sordoni
Self Supervised Learning Self Supervised Learning Method Supervised Classification Expressive Neural

May 31, 2022

Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
Time Series Time Varying Concept Drift Performance Guarantee Local Adaptivity Supervised Classification Instance Level Pseudo Label Minimax Risk Classifier

May 10, 2022

THOR: Threshold-Based Ranking Loss for Ordinal Regression
Tzeviya Sylvia Fuchs, Joseph Keshet
Loss Function Ordinal Regression Supervised Classification Ranking Loss CNN Framework

April 26, 2022

Supervised machine learning classification for short straddles on the S&P500
Alexander Brunhuemer, Lukas Larcher, Philipp Seidl, Sascha Desmettre, Johannes Kofler, Gerhard Larcher
Machine Learning Model Classification Code Supervised Learning Supervised Classification Long Short Portfolio

February 16, 2022

Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, Piotr Bojanowski
Self Supervised Learning Supervised ImageNet Vision Model Unlabeled Image Fine Grained Recognition Image Classification Datasets Supervised Classification

Supervised Classification

Papers

The OCON model: an old but gold solution for distributable supervised classification

Error Bounds of Supervised Classification from Information-Theoretic Perspective

Context-Specific Refinements of Bayesian Network Classifiers

Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction

Probabilistic Safety Regions Via Finite Families of Scalable Classifiers

Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach

A brief review of contrastive learning applied to astrophysics

On the Impact of Data Quality on Image Classification Fairness

Utilizing synthetic training data for the supervised classification of rat ultrasonic vocalizations

Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification

Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network

Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks

Confidence-Guided Learning Process for Continuous Classification of Time Series

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods

Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees

THOR: Threshold-Based Ranking Loss for Ordinal Regression

Supervised machine learning classification for short straddles on the S&P500

Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision