Pattern recognition method

Abstract

According to one embodiment of the present invention, there is provided a pattern recognition method of approximating distribution of a set of vectors and a class boundary in a vector space based on basis functions. The method includes defining directional basis functions between two basis vectors, and performing the approximation using a linear combination of the directional basis functions.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention, and together with the general description given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

FIG. 1 is a flowchart of a processing operation of an embodiment of the present invention, shown as an example;

FIG. 2 is an illustration of the vectors, the basis functions and the distribution of the set of vectors that correspond to an input pattern, shown as an example;

FIG. 3 is an illustration of basis functions, shown as an example;

FIG. 4 is a schematic illustration of an approximation of a pattern distribution, shown as an example;

FIG. 5 is a schematic illustration of an approximation of a pattern distribution same as that of FIG. 4 made using conventional spherical basis functions;

FIG. 6 is a schematic illustration of basis functions when the number of obtained samples is small, shown as an example;

FIG. 7 is a schematic illustration of an approximation of a pattern distribution when the number of obtained samples is small, shown as an example;

FIG. 8 is a schematic illustration of an approximation of the pattern distribution same as FIG. 7 made using conventional spherical basis functions, shown as an example;

FIG. 9 is a flowchart illustrating a method of defining parameters by learning samples; and

FIG. 10 is a schematic block diagram of hardware, showing the configuration thereof as an example.

Claims

1. A pattern recognition method of approximating distribution of a set of vectors and a class boundary in a vector space based on basis functions, comprising: defining directional basis functions between two basis vectors; andperforming the approximation using a linear combination of the directional basis functions.

2. The method according to claim 1, wherein the approximation is performed using basis functions having peaks at the positions of the two basis vectors and connecting them by a non-linear curved surface whose dimensions correspond to the distance between them.

3. The method according to claim 1, wherein the approximation is performed using basis functions having peaks on the line segment connecting the two basis vectors and a cylindrical profile formed by using the peaks as core, extending in directions perpendicular to it and having dimensions corresponding to the distance between them.

4. The method according to claim 1, wherein the parameters of the basis functions are sequentially updated according to an error minimal standard, while inputting samples.