US 11,960,991 B2 · Device and method for training a classifier

Abstract

A computer-implemented method for training a classifier, particularly a binary classifier, for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier. The method includes providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors.

Claims13

Dependent claims

A computer-implemented method for training a classifier for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the method comprising the following steps:

providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications;
and
training the classifier depending on the provided weighting factors;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive”, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative“, AP=″actual positive” and AN=″actual negative“;
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

41 more elements

A computer-implemented method for using a classifier for classifying sensor signals, wherein the classifier is trained to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the training including providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors, the method comprising the following steps:

receiving a sensor signal including data from a sensor;
determining a first input signal which depends on the sensor signal;
and
feeding the first input signal into the classifier to obtain an output signal that characterizes a classification of the first input signal;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive”, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative“, AP=″actual positive” and AN=″actual negative“;
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

42 more elements

A computer-implemented method for using a classifier trained for providing an actuator control signal for controlling an actuator, wherein the classifier is trained to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the training including providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors, the method comprising the following steps:

receiving a sensor signal including data from a sensor;
determining a first input signal which depends on the sensor signal;
feeding the first input signal into the classifier to obtain an output signal that characterizes a classification of the first input signal;
and
determining the actuator control signal depending on the output signal;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive”, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative“, AP=″actual positive” and AN=″actual negative“;
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

43 more elements

A non-transitory machine readable storage medium on which is stored a computer program for training a binary classifier for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the computer program when executed by a computer, causing the computer to perform the following steps:

providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications;
and
training the classifier depending on the provided weighting factors;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″;
and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

42 more elements

A control system for operating an actuator, the control system comprising:

a classifier trained for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the classifier being trained by providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors;
wherein the control system is configured to operate the actuator in accordance with an output of the classifier;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″;
and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

41 more elements

A control system that is configured to use a classifier for classifying sensor signals, wherein the classifier is trained to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the training including providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors, the control system configured to:

receive a sensor signal including data from a sensor;
determine a first input signal which depends on the sensor signal;
and
feed the first input signal into the classifier to obtain an output signal that characterizes a classification of the first input signal;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″;
and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

43 more elements

Dependencies are inferred from the claim text.

Facts

Application

16/950,396Filed 17 Nov 2020

Priority

EP 192128672 Dec 2019

Published

US 2021/0165391 A13 Jun 2021

Granted

16 Apr 2024

Term ends

Not estimatedThe source records don’t support an estimate.

Classification18 codes

G06N 3/08
Neural networks: learning methods
A63F 13/67
Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or…:…
G05B 19/4155
Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to…
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks

Abstract

Claims13

Dependent claims

providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications;
and
training the classifier depending on the provided weighting factors;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive”, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative“, AP=″actual positive” and AN=″actual negative“;
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

41 more elements

receiving a sensor signal including data from a sensor;
determining a first input signal which depends on the sensor signal;
and
feeding the first input signal into the classifier to obtain an output signal that characterizes a classification of the first input signal;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive”, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative“, AP=″actual positive” and AN=″actual negative“;
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

42 more elements

receiving a sensor signal including data from a sensor;
determining a first input signal which depends on the sensor signal;
feeding the first input signal into the classifier to obtain an output signal that characterizes a classification of the first input signal;
and
determining the actuator control signal depending on the output signal;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive”, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative“, AP=″actual positive” and AN=″actual negative“;
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

43 more elements

providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications;
and
training the classifier depending on the provided weighting factors;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″;
and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

42 more elements

A control system for operating an actuator, the control system comprising:

a classifier trained for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier, the classifier being trained by providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors;
wherein the control system is configured to operate the actuator in accordance with an output of the classifier;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″;
and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

41 more elements

receive a sensor signal including data from a sensor;
determine a first input signal which depends on the sensor signal;
and
feed the first input signal into the classifier to obtain an output signal that characterizes a classification of the first input signal;
wherein the non-decomposable metric is given by the formula
Σ
j
⁢
a
j
·
TP
+
b
j
·
TN
+
f
j
(
PP
,
AP
,
PN
,
AN
)
g
j
(
PP
,
AP
,
PN
,
AN
)
,
where aj and bj are scalar values and fj and gj are functions, and TP, TN, PP, PN, AP and AN are entries of the confusion matrix, wherein TP=″true positive″, TN=“true negative”, PP=“predicted positive”, PN=″predicted negative″, AP=″actual positive″ and AN=″actual negative″;
and
wherein the optimization is carried out by finding an equilibrium of a two-player game between a first player and a second player, wherein the first player tries to find first classifications corresponding to input signals of the training data and the second player tries to find second classifications corresponding to input values of the training data, and wherein the first player tries to maximize and the second player tries to minimize an expectation value of a metric in which the confusion matrix is evaluated based on the first classifications and the second classifications, wherein the second classifications are subject to a moment-matching constraint;
and
wherein the expectation value is computed based on marginal probabilities of the first classifications and/or the second classifications.

43 more elements

Dependencies are inferred from the claim text.

Device and method for training a classifier

Life of this patent

Abstract

Claims13

Device and method for training a classifier

Life of this patent

Abstract

Claims13