Add to ORKGWe investigated the geometrical complexity of several high-dimensional, small sample classication problems and its changes due to two popular feature selection procedures, forward feature selection (FFS) and Linear Programming Support Vector Machine (LPSVM). We found that both pro-cedures are able to transform the problems to spaces of very low dimensionality where class separability is improved over that in the original space. The study shows that geo-metrical complexities have good potentials for comparing different feature selection methods in aspects relevant to classication accuracy, yet independent of particular clas-sier choices. 1
OF COMPUTER VISION Most learning systems use hand-picked sets of features as input data for their le...
The problem of discriminating between two finite point sets in n-dimensional feature space by a sepa...
Abstract: Firstly, a distinguishable condition is proposed for separating the features by linear cl...
We investigated the geometrical complexity of several high-dimensional, small sample classification ...
Abstract. Support vector machines (SVMs) rely on the inherent geom-etry of a data set to classify tr...
Computational comparison is made between two feature selection approaches for finding a separating p...
Computational comparison is made between two feature selection approaches for finding a separating p...
Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data...
Abstract. General dissimilarity-based learning approaches have been proposed for dissimilarity data ...
A novel feature selection method based on geometric distance is proposed. It utilises both the avera...
In this work we consider feature selection for two-class linear models, a challenging task arising i...
Abstract. Feature selection is usually motivated by improved computa-tional complexity, economy and ...
DoctorFeature selection is the process of selecting a related subset that affects the performance of...
Classification can often benefit from efficient feature selection. However, the presence of linearly...
We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for cl...
OF COMPUTER VISION Most learning systems use hand-picked sets of features as input data for their le...
The problem of discriminating between two finite point sets in n-dimensional feature space by a sepa...
Abstract: Firstly, a distinguishable condition is proposed for separating the features by linear cl...
We investigated the geometrical complexity of several high-dimensional, small sample classification ...
Abstract. Support vector machines (SVMs) rely on the inherent geom-etry of a data set to classify tr...
Computational comparison is made between two feature selection approaches for finding a separating p...
Computational comparison is made between two feature selection approaches for finding a separating p...
Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data...
Abstract. General dissimilarity-based learning approaches have been proposed for dissimilarity data ...
A novel feature selection method based on geometric distance is proposed. It utilises both the avera...
In this work we consider feature selection for two-class linear models, a challenging task arising i...
Abstract. Feature selection is usually motivated by improved computa-tional complexity, economy and ...
DoctorFeature selection is the process of selecting a related subset that affects the performance of...
Classification can often benefit from efficient feature selection. However, the presence of linearly...
We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for cl...
OF COMPUTER VISION Most learning systems use hand-picked sets of features as input data for their le...
The problem of discriminating between two finite point sets in n-dimensional feature space by a sepa...
Abstract: Firstly, a distinguishable condition is proposed for separating the features by linear cl...