When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
[HTML][HTML] Machine learning for landslides prevention: a survey
Landslides are one of the most critical categories of natural disasters worldwide and induce
severely destructive outcomes to human life and the overall economic system. To reduce its …
severely destructive outcomes to human life and the overall economic system. To reduce its …
[HTML][HTML] Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory
Efforts are increasingly being made to classify the world's wetland resources, an important
ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing …
ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing …
Macular OCT classification using a multi-scale convolutional neural network ensemble
Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical
image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) …
image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) …
Mixture of experts: a literature survey
S Masoudnia, R Ebrahimpour - Artificial Intelligence Review, 2014 - Springer
Mixture of experts (ME) is one of the most popular and interesting combining methods, which
has great potential to improve performance in machine learning. ME is established based on …
has great potential to improve performance in machine learning. ME is established based on …
Semantic content-based image retrieval: A comprehensive study
The complexity of multimedia contents is significantly increasing in the current digital world.
This yields an exigent demand for developing highly effective retrieval systems to satisfy …
This yields an exigent demand for developing highly effective retrieval systems to satisfy …
A brief survey on random forest ensembles in classification model
AB Shaik, S Srinivasan - … : Proceedings of ICICC 2018, Volume 2, 2019 - Springer
Abstract Machine Learning has got the popularity in recent times. Apart from machine
learning the decision tree is one of the most sought out algorithms to classify or predict future …
learning the decision tree is one of the most sought out algorithms to classify or predict future …
A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers
In the present study, Rotation Forest ensemble was integrated with different base classifiers
to develop different hybrid models namely Rotation Forest based Support Vector Machines …
to develop different hybrid models namely Rotation Forest based Support Vector Machines …
A tree-based intelligence ensemble approach for spatial prediction of potential groundwater
M Avand, S Janizadeh, D Tien Bui… - … Journal of Digital …, 2020 - Taylor & Francis
The objective of this research is to propose and confirm a new machine learning approach
of Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), and Bagging (Bag) …
of Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), and Bagging (Bag) …
The study of under-and over-sampling methods' utility in analysis of highly imbalanced data on osteoporosis
Osteoporosis is a frequent bone disease without typical early symptoms but with serious
complications eg low-energy bone fractures. Patients with risk factors should be screened …
complications eg low-energy bone fractures. Patients with risk factors should be screened …