AbstractWe analyze theoretically the generalization properties of multi-class data classification techniques that are based on iteratively partitioning the data points by hyperplanes. A special case is that in which the data points of different classes are separated by a number of parallel hyperplanes, and we investigate the algorithmics of finding a suitable partitioning in this case
The classical Tverberg's theorem says that a set with sufficiently many points in $R^d$ can always b...
The process of computation of classification trees can be characterized as involving three basic cho...
A new upper bound is given on the number of ways in which a set of N points in R n can be partitione...
AbstractWe analyze theoretically the generalization properties of multi-class data classification te...
The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear kern...
This paper concerns the use of threshold decision lists for classifying data into two classes. The u...
This paper concerns the use of threshold decision lists for classifying data into two classes. The u...
Finding a hyperplane that separates two classes of data points with the minimum number of misclassif...
Several popular Machine Learning techniques are originally designed for the solution of two-class pr...
In this paper, we present a novel approach to construct multiclass classifiers by means of arrangeme...
Many machine learning applications employ a multiclass clas-sification stage that uses multiple bina...
The problem of pattern classification is considered for the case of multicategory classification whe...
Abstract In classification, with an increasing number of variables, the required number of observati...
Various popular machine learning techniques, like support vector machines, are originally conceived ...
Abstract—In this paper, we investigate how to design an optimized discriminating order for boosting ...
The classical Tverberg's theorem says that a set with sufficiently many points in $R^d$ can always b...
The process of computation of classification trees can be characterized as involving three basic cho...
A new upper bound is given on the number of ways in which a set of N points in R n can be partitione...
AbstractWe analyze theoretically the generalization properties of multi-class data classification te...
The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear kern...
This paper concerns the use of threshold decision lists for classifying data into two classes. The u...
This paper concerns the use of threshold decision lists for classifying data into two classes. The u...
Finding a hyperplane that separates two classes of data points with the minimum number of misclassif...
Several popular Machine Learning techniques are originally designed for the solution of two-class pr...
In this paper, we present a novel approach to construct multiclass classifiers by means of arrangeme...
Many machine learning applications employ a multiclass clas-sification stage that uses multiple bina...
The problem of pattern classification is considered for the case of multicategory classification whe...
Abstract In classification, with an increasing number of variables, the required number of observati...
Various popular machine learning techniques, like support vector machines, are originally conceived ...
Abstract—In this paper, we investigate how to design an optimized discriminating order for boosting ...
The classical Tverberg's theorem says that a set with sufficiently many points in $R^d$ can always b...
The process of computation of classification trees can be characterized as involving three basic cho...
A new upper bound is given on the number of ways in which a set of N points in R n can be partitione...
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