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An Enhanced Semi-Supervised Support Vector Machine Algorithm for Spectral-Spatial Hyperspectral Image Classification
AbstractHyperspectral image classification has become an important issue in remote sensing due to the significant amount of spectral information in...
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Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classification
For semi-supervised learning, a few labeled data and a large number of unlabeled data are used to construct a reasonable classifier. In recent years,...
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Semi-supervised sparse least squares support vector machine based on Mahalanobis distance
To reflect the similarity of input samples and improve the sparsity of semi-supervised least squares support vector machine (SLSSVM), a novel...
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Tackle balancing constraints in semi-supervised ordinal regression
Semi-supervised ordinal regression (S 2 OR) has been recognized as a valuable technique to improve the performance of the ordinal regression (OR) model...
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An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift
Data streams are important sources of information nowadays, and with the popularization of mobile devices and sensor systems that collect all kinds...
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An improved multi-task least squares twin support vector machine
In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge...
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Semi-supervised machine learning framework for network intrusion detection
Network intrusion detection plays an important role as tools for managing and identifying potential threats, which presents various challenges....
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Semi-supervised Multi-class Classification Methods Based on Laplacian Vector Projection
Laplacian pair-weight vector projection (LapPVP) is a binary classifier for semi-supervised learning, which seeks a pair of projection vectors only...
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A semi-supervised interactive algorithm for change point detection
The goal of change point detection (CPD) is to identify abrupt changes in the statistics of signals or time series that reflect transitions in the...
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Fuzzy Least Squares Support Vector Machine with Fuzzy Hyperplane
This study uses fuzzy set theory for least squares support vector machines (LS-SVM) and proposes a novel formulation that is called a fuzzy...
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Functional iterative approach for Universum-based primal twin bounded support vector machine to EEG classification (FUPTBSVM)
Due to the increasing popularity of support vector machine (SVM) and the introduction of Universum, many variants of SVM along with Universum such as...
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Structured support vector machine with coarse-to-fine PatchMatch filtering for stereo matching
In the past decades, a variety of learning-based algorithms have been emerged to try to explore a better solution for stereo matching by leveraging...
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SemiDocSeg: harnessing semi-supervised learning for document layout analysis
Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table,...
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L1-norm Laplacian support vector machine for data reduction in semi-supervised learning
As a semi-supervised learning method, Laplacian support vector machine (LapSVM) is popular. Unfortunately, the model generated by LapSVM has a poor...
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One novel class of Bézier smooth semi-supervised support vector machines for classification
The semi-supervised support vector machine (S 3 VM) for classification is introduced for dealing with quantities of unlabeled data in the real world....
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BSRU: boosting semi-supervised regressor through ramp-up unsupervised loss
Semi-supervised regression aims to improve the performance of the learner with the help of unlabeled data. Popular approaches select some unlabeled...
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A semi-supervised framework for concept-based hierarchical document clustering
Text clustering is used in various applications of text analysis. In the clustering process, the employed document representation method has a...
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When less is more: on the value of “co-training” for semi-supervised software defect predictors
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training....
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Semi supervised K–SVCR for multi-class classification
In recent developments, the traditional binary class SVM has evolved into a multi-class classifier utilizing a ‘1-versus-1-versus-rest’ approach...
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Kernel induced semi-supervised spatial clustering: a novel brain MRI segmentation technique
Segmentation of different brain tissues such as white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM) form magnetic resonance image...
