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Robust large-scale online kernel learning
The control-based approach has been proved to be effective for develo** robust online learning methods. However, the existing control-based kernel...
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Uncalibrated visual servoing based on Kalman filter and mixed-kernel online sequential extreme learning machine for robot manipulator
Visual servoing systems may suffer from interference by system noise when a Kalman filter is used to obtain a Jacobian matrix. Such interference may...
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Online Hybrid Kernel Learning Machine with Dynamic Forgetting Mechanism
This paper, for the purpose of meeting challenges of fewer resources of storage and calculation in the detection of ICS intrusion as well as...
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Online local fisher risk minimization: a new online kernel method for online classification
This study presents a new online kernel algorithm for online classification, called the online local Fisher rick minimization (OLFRM). Motivated by...
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Isolation kernel: the X factor in efficient and effective large scale online kernel learning
Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially...
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Sparse online kernelized actor-critic Learning in reproducing kernel Hilbert space
In this paper, we develop a novel non-parametric online actor-critic reinforcement learning (RL) algorithm to solve optimal regulation problems for a...
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FFTRL: A Sparse Online Kernel Classification Algorithm for Large Scale Data
Online kernel learning is an efficient way when dealing with nonlinearly large-scale data. The training speed of online kernel learning is improved...
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Online Network Source Optimization with Graph-Kernel MAB
We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the...
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Collaborative and dynamic kernel discriminant analysis for large-scale problems: applications in multi-class learning and novelty detection
We present CKDA a new multi-class collaborative learning strategy based on multiple kernel discriminant analysis learners. The principle of CKDA is...
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Nonparametric Bayesian online change point detection using kernel density estimation with nonparametric hazard function
This paper aims to develop Bayesian online change point detection (BOCD), a parametric change point detection method, into a nonparametric method to...
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A low-cost, high-throughput neuromorphic computer for online SNN learning
Neuromorphic devices capable of training spiking neural networks (SNNs) are not easy to develop due to two main factors: lack of efficient supervised...
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A deep learning based approach for classifying tweets related to online learning during the Covid-19 pandemic
The majority of educational institutions around the world have switched to online learning due to the COVID-19 pandemic. Since continuing education...
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Worst-case regret analysis of computationally budgeted online kernel selection
We study the problem of online kernel selection under computational constraints, where the memory or time of kernel selection and online prediction...
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Online concept evolution detection based on active learning
Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient...
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Kernel-based multiagent reinforcement learning for near-optimal formation control of mobile robots
Feature representation is a major issue to be addressed for learning-based control of multiagent systems. In this paper, a kernel-based multiagent...
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EEG-based Emotion Recognition Using Multiple Kernel Learning
Emotion recognition based on electroencephalography (EEG) has a wide range of applications and has great potential value, so it has received...
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Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking
The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This...
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Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learning
Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges...
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Performance prediction in online academic course: a deep learning approach with time series imaging
With the COVID-19 outbreak, schools and universities have massively adopted online learning to ensure the continuation of the learning process....
