Feature selection and feature learning in machine learning applications for gas turbines: A review
The progress of machine learning (ML) in the past years has opened up new opportunities
to the field of gas turbine (GT) modelling. However, successful implementation of ML …
to the field of gas turbine (GT) modelling. However, successful implementation of ML …
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
A survey on semi-supervised feature selection methods
Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …
which eliminates irrelevant and redundant features and improves learning performance. In …
A pragmatic investigation of energy consumption and utilization models in the urban sector using predictive intelligence approaches
Energy consumption is a crucial domain in energy system management. Recently, it was
observed that there has been a rapid rise in the consumption of energy throughout the …
observed that there has been a rapid rise in the consumption of energy throughout the …
Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis
Fiber-reinforced self-compacting concrete (FRSCC), a great combination of self-compacting
concrete (SCC) and fiber, plays a vital role as a potential construction material. Improving …
concrete (SCC) and fiber, plays a vital role as a potential construction material. Improving …
On the platform but will they buy? Predicting customers' purchase behavior using deep learning
A thorough understanding of online customer's purchase behavior will directly boost e-
commerce business performance. Existing studies have overtly focused on purchase …
commerce business performance. Existing studies have overtly focused on purchase …
Robust large margin deep neural networks
The generalization error of deep neural networks via their classification margin is studied in
this paper. Our approach is based on the Jacobian matrix of a deep neural network and can …
this paper. Our approach is based on the Jacobian matrix of a deep neural network and can …
Unsupervised feature selection via nonnegative spectral analysis and redundancy control
Z Li, J Tang - IEEE Transactions on Image Processing, 2015 - ieeexplore.ieee.org
In many image processing and pattern recognition problems, visual contents of images are
currently described by high-dimensional features, which are often redundant and noisy …
currently described by high-dimensional features, which are often redundant and noisy …
EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate
KQ Shen, XP Li, CJ Ong, SY Shao… - Clinical …, 2008 - Elsevier
OBJECTIVE: Automatic measurement and the monitoring of mental fatigue are invaluable for
preventing mental-fatigue related accidents. We test an EEG-based mental-fatigue …
preventing mental-fatigue related accidents. We test an EEG-based mental-fatigue …
A robust least squares support vector machine for regression and classification with noise
Least squares support vector machines (LS-SVMs) are sensitive to outliers or noise in the
training dataset. Weighted least squares support vector machines (WLS-SVMs) can partly …
training dataset. Weighted least squares support vector machines (WLS-SVMs) can partly …