International audienceWe present a near linear algorithm for determining the linear separability of two sets of points in a two-dimensional space. That algorithm does not only detects the linear separability but also computes separation information. When the sets are linearly separable, the algorithm provides a description of a separation hyperplane. For non linearly separable cases, the algorithm indicates a negative answer and provides a hyperplane of partial separation that could be useful in the building of some classification systems
This article presents an analysis of some of the methods for testing linear separability. A single l...
The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real spa...
This thesis deals with a pattern classification problem, which geometrically implies data separation...
A geometric and non parametric procedure for testing if two finite set of points are linearly separa...
Efficient linear separation algorithms are important for pattern classification applications. In thi...
We prove new theorems which describe a necessary and sufficient condition for linear (strong and non...
AbstractGiven linearly inseparable sets R of red points and B of blue points, we consider several me...
Learning the separating hyper-planes between given sets of vectors have numerous application areas s...
The paper presents a recursive algorithm for the investigation of a strict, linear separation in the...
We consider the problem of discriminating two finite point sets in the n-dimensional space by a fini...
This paper introduces latest advances in the subject of linear separability. New methods for testing...
A single linear program is proposed for discriminating between the elements of k disjoint point sets...
We develop exact and approximate algorithms for computing optimal separators and measuring the exten...
24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Finan...
Linear separability of data sets is one of the basic concepts in the theory of neural networks and p...
This article presents an analysis of some of the methods for testing linear separability. A single l...
The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real spa...
This thesis deals with a pattern classification problem, which geometrically implies data separation...
A geometric and non parametric procedure for testing if two finite set of points are linearly separa...
Efficient linear separation algorithms are important for pattern classification applications. In thi...
We prove new theorems which describe a necessary and sufficient condition for linear (strong and non...
AbstractGiven linearly inseparable sets R of red points and B of blue points, we consider several me...
Learning the separating hyper-planes between given sets of vectors have numerous application areas s...
The paper presents a recursive algorithm for the investigation of a strict, linear separation in the...
We consider the problem of discriminating two finite point sets in the n-dimensional space by a fini...
This paper introduces latest advances in the subject of linear separability. New methods for testing...
A single linear program is proposed for discriminating between the elements of k disjoint point sets...
We develop exact and approximate algorithms for computing optimal separators and measuring the exten...
24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Finan...
Linear separability of data sets is one of the basic concepts in the theory of neural networks and p...
This article presents an analysis of some of the methods for testing linear separability. A single l...
The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real spa...
This thesis deals with a pattern classification problem, which geometrically implies data separation...