A novel robust kernel for classifying high-dimensional data using Support Vector Machines

2019 ◽  
Vol 131 ◽  
pp. 116-131 ◽  
Author(s):  
Syed Fawad Hussain
Keyword(s):  
Support Vector Machines ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Support Vector ◽  
Vector Machines

scholarly journals Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines

2003 ◽  
Vol 7 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Sadaaki Miyamoto ◽  
◽  
Youichi Nakayama ◽  
Keyword(s):  
Support Vector Machines ◽  
Dimensional Space ◽  
Iterative Algorithms ◽  
Kernel Functions ◽  
High Dimensional ◽  
Support Vector ◽  
High Dimensional Space ◽  
Numerical Examples ◽  
Vector Machines

We discuss hard c-means clustering using a mapping into a high-dimensional space considered within the theory of support vector machines. Two types of iterative algorithms are developed. Effectiveness of the proposed method is shown by numerical examples.

Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets

2015 ◽  
Vol 42 (23) ◽  
pp. 9183-9191 ◽  
Author(s):  
Vijay Pappu ◽  
Orestis P. Panagopoulos ◽  
Petros Xanthopoulos ◽  
Panos M. Pardalos
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
High Dimensional ◽  
Support Vector ◽  
Vector Machines ◽  
High Dimensional Datasets

Efficient nearest neighbors methods for support vector machines in high dimensional feature spaces

2020 ◽  
Author(s):  
Diana C. Montañés ◽  
Adolfo J. Quiroz ◽  
Mateo Dulce Rubio ◽  
Alvaro J. Riascos Villegas
Keyword(s):  
Support Vector Machines ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Support Vector ◽  
Feature Spaces ◽  
Vector Machines

scholarly journals Analyzing high dimensional correlated data using feature ranking and classifiers

2019 ◽  
Vol 7 (1) ◽  
pp. 98-120
Author(s):  
Abhijeet R Patil ◽  
Jongwha Chang ◽  
Ming-Ying Leung ◽  
Sangjin Kim
Keyword(s):  
Information Gain ◽  
High Dimensional Data ◽  
Real Data ◽  
Correlated Data ◽  
High Dimensional ◽  
Support Vector ◽  
Feature Ranking ◽  
Fisher Score ◽  
Chi Square ◽  
Vector Machines

AbstractThe Illumina Infinium HumanMethylation27 (Illumina 27K) BeadChip assay is a relatively recent high-throughput technology that allows over 27,000 CpGs to be assayed. The Illumina 27K methylation data is less commonly used in comparison to gene expression in bioinformatics. It provides a critical need to find the optimal feature ranking (FR) method for handling the high dimensional data. The optimal FR method on the classifier is not well known, and choosing the best performing FR method becomes more challenging in high dimensional data setting. Therefore, identifying the statistical methods which boost the inference is of crucial importance in this context. This paper describes the detailed performances of FR methods such as fisher score, information gain, chi-square, and minimum redundancy and maximum relevance on different classification methods such as Adaboost, Random Forest, Naive Bayes, and Support Vector Machines. Through simulation study and real data applications, we show that the fisher score as an FR method, when applied on all the classifiers, achieved best prediction accuracy with significantly small number of ranked features.

scholarly journals Applying machine learning methods to avalanche forecasting

2008 ◽  
Vol 49 ◽  
pp. 107-113 ◽  
Author(s):  
A. Pozdnoukhov ◽  
R.S. Purves ◽  
M. Kanevski
Keyword(s):  
Machine Learning ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Support Vector ◽  
Avalanche Forecasting ◽  
Machine Learning Methods ◽  
Multiple Data ◽  
Complex Process ◽  
Probabilistic Forecasts ◽  
Vector Machines

AbstractAvalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest-neighbour methods (NN), which are known to have limitations when dealing with high-dimensional data. We apply support vector machines (SVMs) to a dataset from Lochaber, Scotland, UK, to assess their applicability in avalanche forecasting. SVMs belong to a family of theoretically based techniques from machine learning and are designed to deal with high-dimensional data. Initial experiments showed that SVMs gave results that were comparable with NN for categorical and probabilistic forecasts. Experiments utilizing the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Sign in / Sign up

Export Citation Format

Share Document