Online learning: A comprehensive survey
Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …
to tackle some predictive (or any type of decision-making) task by learning from a sequence …
Online multi-agent forecasting with interpretable collaborative graph neural networks
This article considers predicting future statuses of multiple agents in an online fashion by
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …
Random feature-based online multi-kernel learning in environments with unknown dynamics
Kernel-based methods exhibit well-documented performance in various nonlinear learning
tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task …
tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task …
Large scale online multiple kernel regression with application to time-series prediction
Kernel-based regression represents an important family of learning techniques for solving
challenging regression tasks with non-linear patterns. Despite being studied extensively …
challenging regression tasks with non-linear patterns. Despite being studied extensively …
Distributed and quantized online multi-kernel learning
Kernel-basedlearning has well-documented merits in various machine learning tasks. Most
of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which …
of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which …
Learning with incremental instances and features
In many real-world applications, data may dynamically expand over time in both volume and
feature dimensions. Besides, they are often collected in batches (also called blocks). We …
feature dimensions. Besides, they are often collected in batches (also called blocks). We …
Online portfolio selection with predictive instantaneous risk assessment
Online portfolio selection (OPS) has received increasing attention from machine learning
and quantitative finance communities. Despite their effectiveness, the pioneering OPS …
and quantitative finance communities. Despite their effectiveness, the pioneering OPS …
Online learning method based on support vector machine for metallographic image segmentation
M Li, D Chen, S Liu, D Guo - Signal, Image and Video Processing, 2021 - Springer
The shape, size and distribution of the microstructure could certainly reveal mechanical
properties. Therefore, it is important to segment the microstructure accurately. However, in …
properties. Therefore, it is important to segment the microstructure accurately. However, in …
Online multi-view subspace learning with mixed noise
Multi-view learning reveals the latent correlation between different input modalities and has
achieved outstanding performances in many fields. Recent approaches aim to find a low …
achieved outstanding performances in many fields. Recent approaches aim to find a low …
An Online Two-Stage Classification Based on Projections
Kernel-based online classification algorithms, such as the Perceptron, NORMA, and passive-
aggressive, are renowned for their computational efficiency but have been criticized for slow …
aggressive, are renowned for their computational efficiency but have been criticized for slow …