Sep 16, 2024 · TL;DR: I conducted a case study on the MNIST dataset and boosted my model’s accuracy from 90 % to 95 % by applying iterative, confidence-based pseudo-labeling. This …

May 31, 2024 · Active learning empowers machine learning algorithms to become more strategic in their learning process. The core principle lies in enabling the algorithm to query and select …

Nov 14, 2023 · 6. Active Learning: An Iterative Data Labeling Approach. Active learning is an iterative process that improves data labeling efficiency. Initially, a smaller subset of data is …

Semi-supervised learning can be paired with active learning to create a powerful iterative process. Initially, a model can be trained on a small labeled dataset to make predictions on unlabeled …

Mar 1, 2019 · Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling …

Jul 8, 2022 · One of the shining successes in machine learning is the gradient descent algorithm (and its modified counterpart, stochastic gradient descent). Gradient descent is an iterative …

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.

Aug 17, 2023 · Active Learning: This approach involves the iterative process of selecting data points that the model is uncertain about and having human annotators label those points. This …

Iterative stratification for multi-label data. The classifier follows methods outlined in Sechidis11 and Szymanski17 papers related to stratyfing multi-label data.

We aim to avoid the traverse of the entire dataset by teaching the learner through label synthesis instead of example selection. The red dottedframes indicate the teacher’s efforts. Why label …