Patent ID: 7773800

Claim:
A process for recognizing a digital image using a computer and an ABM algorithm, where ABM is the “Attrasoft Boltzmann Machine”, which further consists of the ABM training algorithm and the ABM recognition algorithm, said ABM training algorithm comprises: Imposing an image to an ABM so the ABM will be trained, whereas the ABM is a specific combination of (a) a fully connected neural network and (b) a Markov chain; Classifying at least one target image based on the invariant distribution function of the trained ABM wherein the step of Imposing an image to an ABM further comprises: a) deleting existing ABM connections; b) creating an input vector, p, based on an input image, x, and its classification, y; c) breaking the input vector, p, into a number of pieces, p1, p2, p3 . . . , where such breaking could either be logically based on objects/segments or geometrically; d) constructing a set of neural state vectors, s1, s2, s3 . . . according to p1, p2, p3 . . . , whereas a state vector, s1, has a number of 0's (grounded state) and a number of 1's (excited state); all such vectors together form a configuration space, H(P); e) computing an initial neural connection from each of p1, p2, p3 . . . , said computation comprising: e1) constructing a connection space, H(C), where each neural connection is a point inside this space; e2) making the connection space, H(C), from a configuration space, H(C)=(H(P), R), where R is a space of real numbers; e3) making an initial connection c1 to be c1=(p1, 1), or f(p1)=1, where f(p1) is a connection matrix element; f) computing the rest of the neural connections from each of the initial connections, c1, c2, c3 . . . , said computation comprising: f1) constructing a distance or distances, d(p1, p1′), between an initial neural state, p1, and an arbitrary state, p1′; said distances can be Hausdorff distance, or L1 distance, or L2 distance, or any other distances; f2) constructing a function, g(d), which maps a distance between two neural vectors, d, to a number, g(d); said function comprising of any functions as long as it decreases in value when the distance increases; f3) constructing an arbitrary connection element (p1′, g(d(p1, p1′))) from the initial connection element; f4) applying (p1′, g(d(p1, p1′))) for all points in the connection space since the ABM is a fully connected network with all possible ranks; and g) constructing an ABM Markov chain after all of the connections are established.