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A survey on active learning: State-of-the-art, practical challenges and research directions
Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …
[HTML][HTML] A survey on machine learning for recurring concept drifting data streams
The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …
Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature
This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …
[HTML][HTML] Unsupervised real-time anomaly detection for streaming data
We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …
Largely driven by the rise of connected real-time data sources, this data presents technical …
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey
Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …
availability of a historical dataset for model development, and that the resulting model will, to …
Recent advances in neuro-fuzzy system: A survey
Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific
and engineering areas due to its effective learning and reasoning capabilities. The neuro …
and engineering areas due to its effective learning and reasoning capabilities. The neuro …
Meta-ADD: A meta-learning based pre-trained model for concept drift active detection
Abstract Concept drift is a phenomenon that commonly happened in data streams and need
to be detected, because it means the statistical properties of a target variable, which the …
to be detected, because it means the statistical properties of a target variable, which the …
Action recognition based on joint trajectory maps with convolutional neural networks
Abstract Convolutional Neural Networks (ConvNets) have recently shown promising
performance in many computer vision tasks, especially image-based recognition. How to …
performance in many computer vision tasks, especially image-based recognition. How to …
A large-scale comparison of concept drift detectors
Online learning involves extracting information from large quantities of data (streams)
usually affected by changes in the distribution (concept drift). A drift detector is a small …
usually affected by changes in the distribution (concept drift). A drift detector is a small …
Smart grid load forecasting using online support vector regression
Smart grid, an integral part of a smart city, provides new opportunities for efficient energy
management, possibly leading to big cost savings and a great contribution to the …
management, possibly leading to big cost savings and a great contribution to the …