High Dimension Low Sample Size statistical analysis is becoming increasingly important in a wide range of applied contexts. In such situations, it is seen that the appealing discrimination method called the Support Vector Machine can be improved. The revealing concept is data piling at the margin. This leads naturally to the development of Distance Weighted Discrimination,whichalsoisbasedonmoderncomputationally intensive optimization methods, and seems to give improved generalizability
A typical machine learning algorithm takes advantage of training data to discover patterns among obs...
Avuçlu, Emre ( Aksaray, Yazar )Today, machine learning algorithms are an important research area cap...
The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervi...
High Dimension Low Sample Size statistical analysis is becoming in-creasingly important in a wide ra...
While Distance Weighted Discrimination (DWD) is an appealing approach to classifica-tion in high dim...
While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dime...
While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dime...
While Distance-Weighted Discrimination (DWD) is an appealing approach to classifi-cation in high dim...
Appropriate training data always play an important role in constructing an efficient classifier to s...
Linear discriminant analysis has gained extensive applications in supervised classification and dime...
The nearest neighbor technique is a simple and appealing approach to addressing classification probl...
The goal of machine learning is to build automated systems that can classify and recognize com-plex ...
This paper is concerned with screening features in ultrahigh dimensional data anal-ysis, which has b...
Classification is an important supervised learning technique with numerous applications. This disser...
Traditionally, shape analysis is mostly used in representation and statistical analysis of single ob...
A typical machine learning algorithm takes advantage of training data to discover patterns among obs...
Avuçlu, Emre ( Aksaray, Yazar )Today, machine learning algorithms are an important research area cap...
The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervi...
High Dimension Low Sample Size statistical analysis is becoming in-creasingly important in a wide ra...
While Distance Weighted Discrimination (DWD) is an appealing approach to classifica-tion in high dim...
While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dime...
While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dime...
While Distance-Weighted Discrimination (DWD) is an appealing approach to classifi-cation in high dim...
Appropriate training data always play an important role in constructing an efficient classifier to s...
Linear discriminant analysis has gained extensive applications in supervised classification and dime...
The nearest neighbor technique is a simple and appealing approach to addressing classification probl...
The goal of machine learning is to build automated systems that can classify and recognize com-plex ...
This paper is concerned with screening features in ultrahigh dimensional data anal-ysis, which has b...
Classification is an important supervised learning technique with numerous applications. This disser...
Traditionally, shape analysis is mostly used in representation and statistical analysis of single ob...
A typical machine learning algorithm takes advantage of training data to discover patterns among obs...
Avuçlu, Emre ( Aksaray, Yazar )Today, machine learning algorithms are an important research area cap...
The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervi...