Source: https://insight.rpxcorp.com/pat/US5638491A
Timestamp: 2020-04-07 00:53:34
Document Index: 651233604

Matched Legal Cases: ['art 410', 'art 412', 'art 410', 'art 412', 'art 410', 'art 412', 'art 410', 'art 412', 'art 410', 'art 410', 'art 410', 'art 410', 'art 410', 'art 410', 'art 412', 'art 412', 'art 412']

Patent US 5,638,491 A
Filed: 01/23/1995
1. A method for classifying an input into one of a plurality of possible outputs, comprising the steps of:
(a) applying a top-level classifier to the input to generate an approximate identification for the input as one of the possible outputs;
(b) using the approximate identification to select a subset of available cluster classifiers;
(c) applying said selected cluster classifiers to the input to generate two or more identifications for the input as one or more of the possible outputs; and
(d) classifying the input as one of the possible outputs in accordance with said two or more identifications.
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each of the cluster classifiers employs one or more neurons; and
at least two of the cluster classifiers share a common output.
3. The method of claim 1, wherein step (a) comprises the step of implementing K-means clustering.
7. A system for classifying an input into one of a plurality of possible outputs, comprising:
(a) means for applying a top-level classifier to the input to generate an approximate identification for the input as one of the possible outputs;
(b) means for using the approximate identification to select a subset of available cluster classifiers;
(c) means for applying said selected cluster classifiers to the input to generate two or more identifications for the input as one or more of the possible outputs; and
(d) means for classifying the input as one of the possible outputs in accordance with said two or more identifications.
at least two of the share a common output.
9. The system of claim 7, wherein means (a) comprises means for implementing K-means clustering.
13. A system for classifying an input into one of a plurality of possible outputs, comprising:
(a) a top-level classifier;
(b) a plurality of cluster classifiers; and
(c) a postprocessor, wherein;
the top-level classifier generates an approximate identification for the input as one of the possible outputs and uses the approximate identification to select a subset of the cluster classifiers;
the selected cluster classifiers generate two or more identifications for the input as one or more of the possible outputs; and
the postprocessor classifies the input as one of the possible outputs in accordance with the two or more identifications of the cluster classifiers.
each of the cluster classifiers comprises one or more neurons; and
In a preferred embodiment of the present invention, the neurons are hyper-ellipses in the k-dimensional feature space. A hyper-ellipse is any hyper-volume defined by Equation (1), where (m=2) and (A=1). More particularly, a hyper-ellipse is defined by the function: ##EQU3## where cj define the hyper-ellipse center point, bj are the hyper-ellipse axis lengths, and the values gj that satisfy Equation (3) define the points that lie within or on the hyper-ellipse boundary. When all of the axes are the same length, the hyper-ellipse is a hyper-sphere. In a preferred embodiment of the present invention, in at least one of the neurons, at least two of the axes are of different length. By way of example, there is shown in FIG. 2 elliptical neuron 1, having center point (c01, c11), and axis b01, b11 of different length. In a preferred embodiment, the axes of the neurons are aligned with the coordinate axes of the feature space. Those skilled in the art will understand that other neurons having axes that do not all align with the feature-space axes are within the scope of the invention.
In a preferred embodiment of the present invention, classification system 300 classifies optically acquired character bitmaps using a network of hyper-elliptical neurons. Means 302 of classification system 300 receives as input the bitmap of an optically acquired character image to be classified and generates a corresponding feature vector F. Means 304 then determines an "elliptical distance" rx as a function of the center and axes of each of the Enum hyper-elliptical neurons x in the network and feature vector F, where: ##EQU4## In Equation (4), cjx and bjx define the center point and axis lengths, respectively, of neuron x, where x runs from 0 to Enum -1, and ƒj are the elements of feature vector F. Those skilled in the art would recognize that distance measures different from that of Equation (4) may also be used.
Output level 406 includes two parts: output-total part 410 and output-minimize part 412. Output-total part 410 contains the set Ot of s output processing units ont, and output-minimize part 412 contains the set Om of s output processing units onm, where n runs from 0 to s-1, where s is also the number of possible outputs for which classification system 400 has been trained. For example, when classifying capital letters, s=26. Each processing unit pair (ont,onm) is associated with only one possible output and vice versa.
Each elliptical processing unit ex of processing level 404 is connected to and provides output to only one output processing unit ont of output-total part 410 and to only one output processing unit onm of output-minimize part 412. However, each output processing unit ont and each output processing unit onm may be connected to and receive input from one or more elliptical processing units ex of processing level 404. These relationships are represented by connection matrices Wt and Wm, both of which are of dimension (s ×Enum). In a preferred embodiment, if there is a connection between elliptical processing unit ex of processing level 400 and output processing unit ont of output-total part 410 of output level 406, an entry wnxt in connection matrix Wt will have a value that is equal to the number of training input feature vectors encompassed by neuron x; otherwise, it has value 0. In a further preferred embodiment, entry wnxt has a value 1 if there is a connection between elliptical processing unit ex and output processing unit Ont.
Connection matrix Wm represents the connections between processing level 404 and output-minimize part 412 of output level 406 and is related to connection matrix Wt. An entry wnxm in connection matrix Wm will have a value of 1 for every entry wnxt in connection matrix Wt that is not zero. Otherwise, entry wnxm will have a value of 0.
Each output processing unit ont in output-total part 410 computes an output value ont, where: ##EQU5## where the function T(rx) returns the value 0 if (rx >1); otherwise, it returns the value 1. In other words, the function T(rx) returns the value 1 if elliptical processing unit ex of processing level 404 is activated. Output processing unit ont counts the votes for the possible output with which it is associated and outputs the total. Output-total part 410 of output level 406 is associated with means 306 and means 310 of classification system 300.
Postprocessing level 408 includes two postprocessing units pt and pm. Postprocessing unit pt is connected to and receives input from every output processing unit ont of output-total part 410 of output level 406. Postprocessing unit pt finds the output processing unit ont that has the maximum output value and generates the value qt. If output processing unit ont of output-total part 410 has an output value greater than those of all the other output processing units of output-total part 410, then the value qt is set to n--the index for that output processing unit. For example, when classifying capital letters n may be O for "A" and 1 for "B", etc. Otherwise, the value qt is set to -1 to indicate that output-total part 410 of output level 406 did not classify the input. Postprocessing unit pt of postprocessing level 408 is associated with means 312 of classification system 300.
Similarly, postprocessing unit pm --the other postprocessing unit in postprocessing level 408--is connected to and receives input from every output processing unit onm of output-minimize part 412 of output level 406. Postprocessing unit pm finds the output processing unit onm that has the minimum output value and generates the value qm. If output processing unit onm of output-minimize part 412 has an output value less than a specified threshold Θm, then the value qm is set to the corresponding index n. Otherwise, the value qm is set to -1to indicate that output-minimize part 412 of output level 406 did not classify the input, because the feature vector F is outside the threshold region surrounding neuron x for all neurons x. The threshold Θm may be the same threshold Θm used in classification system 300 of FIG. 3. Postprocessing unit pm of postprocessing level 408 is associated with means 316 of classification system 300.
In alternative preferred embodiment, training system 500 spatially adjusts the boundary of a hyper elliptical neuron to exclude a particular feature vector by shrinking proportionally along one or more axes. Means 510 and 522 of training system 500 may perform proportional shrinking by calculating the vector ΔB of axis length changes Δbj, where: ##EQU10## where .linevert split.ƒ0 -c0 .linevert split. is the absolute value of (ƒ0 -c0);.linevert split..linevert split.F-C.linevert split..linevert split. is the magnitude of the vector difference between F and C; cj and bj define the center point and axis lengths, respectively, of the neuron to be adjusted; ƒj are the elements of the feature vector to be excluded from that neuron; and α and γj may be constants. The new axis lengths bjl for the adjusted neuron are calculated by:
b.sub.j.sup.l =b.sub.j +Δb.sub.j                     (22)
Moed, Michael C.
395/23, 395/20, 395/24, 395/11, 382/156