Add to ORKGThis paper illustrates how feature grouping can improve matching. Obviously feature groupes convey more information and therefore are less ambiguous. But still remains the problem of the choice of these groups, how to build them automatically and how to use them in different problems. Partial answers taken from our work and from some others are given here
Classification of data crosses different domains has been extensively researched and is one of the b...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
The traditional motivation behind feature selection al-gorithms is to nd the best subset of features...
This paper illustrates how feature grouping can improve matching. Obviously feature groupes convey m...
Feature grouping has been demonstrated to be promising in learning with high-dimensional data. It he...
In this report we introduce a classification system named Grouping of Features (GoF), together with ...
This paper introduces concepts and algorithms of feature selection, surveys existing feature selecti...
Feature selection has become an interesting research topic in recent years. It is an effective metho...
In the past two decades, the dimensionality of datasets involved in machine learning and data mining...
Standard feature selection algorithms deal with given candidate feature sets at the individual featu...
Most current feature selection techniques are focused on the incremental inclusion or exclusion of s...
Feature selection aims to choose a subset of features, out of a set of candidate features, such that...
We present an information theoretic approach to feature selection when the data possesses feature cl...
Vision programming is defined as the task of constructing explicit object models to be used in objec...
Analyzing high-dimensional data stands as a great challenge in machine learning. In order to deal wi...
Classification of data crosses different domains has been extensively researched and is one of the b...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
The traditional motivation behind feature selection al-gorithms is to nd the best subset of features...
This paper illustrates how feature grouping can improve matching. Obviously feature groupes convey m...
Feature grouping has been demonstrated to be promising in learning with high-dimensional data. It he...
In this report we introduce a classification system named Grouping of Features (GoF), together with ...
This paper introduces concepts and algorithms of feature selection, surveys existing feature selecti...
Feature selection has become an interesting research topic in recent years. It is an effective metho...
In the past two decades, the dimensionality of datasets involved in machine learning and data mining...
Standard feature selection algorithms deal with given candidate feature sets at the individual featu...
Most current feature selection techniques are focused on the incremental inclusion or exclusion of s...
Feature selection aims to choose a subset of features, out of a set of candidate features, such that...
We present an information theoretic approach to feature selection when the data possesses feature cl...
Vision programming is defined as the task of constructing explicit object models to be used in objec...
Analyzing high-dimensional data stands as a great challenge in machine learning. In order to deal wi...
Classification of data crosses different domains has been extensively researched and is one of the b...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
The traditional motivation behind feature selection al-gorithms is to nd the best subset of features...