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Identification of Hammerstein Systems with Random Fourier Features and Kernel Risk Sensitive Loss
Identification of Hammerstein systems with polynomial features and mean square error loss has received a lot of attention due to their simplicity in...
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A quantized minimum kernel risk hyperbolic secant adaptive filtering algorithm
The proposed algorithm in this paper is the quantized minimum kernel risk hyperbolic secant adaptive filtering algorithm, which offers a simplified...
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Minimum variance embedded auto-associative kernel extreme learning machine for one-class classification
One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning...
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Per-user network access control kernel module with secure multifactor authentication
Network attacks, such as botnets stealing sensitive data, constitute a critical concern for administrators in the Internet area. Such attacks can be...
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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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Risk Prediction of Diabetic Readmission Based on Cost Sensitive Convolutional Neural Network
Diabetes is a chronic disease that nearly affects people of all ages. Some scholars find that the potential risk of diabetes can be effectively...
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Semi-supervised Kernel Fisher discriminant analysis based on exponential-adjusted geometric distance
Fisher discriminant analysis (FDA) is a widely used dimensionality reduction tool in pattern recognition. However, FDA cannot obtain an optimal...
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Random forest kernel for high-dimension low sample size classification
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text...
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Kernel-based regression via a novel robust loss function and iteratively reweighted least squares
Least squares kernel-based methods have been widely used in regression problems due to the simple implementation and good generalization performance....
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LDW-RS Loss: Label Density-Weighted Loss with Ranking Similarity Regularization for Imbalanced Deep Fetal Brain Age Regression
Estimation of fetal brain age based on sulci by magnetic resonance imaging (MRI) is crucial in determining the normal development of the fetal brain....
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Twin Bounded Support Vector Machine with Capped Pinball Loss
In order to obtain a more robust and sparse classifier, in this paper, we propose a novel classifier termed as twin bounded support vector machine...
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Regularization in Reproducing Kernel Hilbert Spaces
Methods for obtaining a function g in a relationship \(y=g(x)\)...
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A Hybrid Model Integrating Improved Fuzzy c-means and Optimized Mixed Kernel Relevance Vector Machine for Classification of Coal and Gas Outbursts
The class labels of collected coal and gas outbursts sample data may be wrong, if these collected sample data are directly used for outbursts...
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Target re-location kernel correlation filtered visual tracking with fused deep feature
To enhance the tracking robustness of the kernel correlation filtering algorithm in complex scenarios, the present study combines the traditional...
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Robust Fisher-regularized extreme learning machine with asymmetric Welsch-induced loss function for classification
In general, it is a worth challenging problem to build a robust classifier for data sets with noises or outliers. Establishing a robust classifier is...
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Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI
Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta...
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Nonparallel Support Vector Machine with L2-norm Loss and its DCD-type Solver
The mechanism of L2-norm loss can be explained from the perspective of maximizing margin and minimizing margin variance, which is equivalent to the...
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Non-linear kernel-based error function for extended Kalman filter oriented robust control of cancer chemotherapy
Amongst the various treatment models, chemotherapy is the most significant and widely practiced to cure cancer. More computerized arithmetical...
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Kernel machines for current status data
In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring....
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A robust projection twin support vector machine with a generalized correntropy-based loss
The projection twin support vector machine (PTSVM) is a potential tool for classification problem. However the loss function of PTSVM is hinge loss...
