THE CONSTRUCTION OF A RECTIFYING SPACE FOR PATTERN RECOGNITION,
Abstract
Automata termed 'artrons' (artificial neurons, i.e. neuron models or mathematical models of threshold elements), which have n binary inputs, a binary output and are capable of learning how to separate input situations into two classes, have recently begun to be often employed for pattern recognition. Two possible cases are considered. Either the reception field can be compared with coordinate unit vectors in Euclidean n-variate space X, such that the various input situations forming the set of nodes of the n-variate cube will be separated by a hyperplane in X (case 1), or the classes in X are not separated by a hyperplane and then transition from the X-space to the so-called 'rectifying' Z-space, in which separation of these classes by a hyperplane is feasible, is needed (case 2). The article examines certain aspects of the construction of the Z-space. The n-th degree polynomial is proposed as the logic separating function.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 12, 1969
- Accession Number
- AD0696499
Entities
People
- Vl. I. Belyakov-bodin
Organizations
- National Air and Space Intelligence Center