A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

An information processing apparatus in the present invention includes: an acquisition unit that, based on registration information including a plurality of registrants and registration time associated with each of the plurality of registrants and on the current time, acquires a first biometrics information group including biometrics information on a candidate of a matching process from a registered biometrics information group including biometrics information on the plurality of registrants; and a matching unit that matches biometrics information on a person detected from an image captured in a matching area against biometrics information included in the first biometrics information group.

Described techniques for multi-label classification, in which sequential data includes characters that have two or more aspects that require classification, are capable of providing separate classifications for different categories of components. Using an appropriately-trained neural network, the described techniques perform aligning and otherwise combining two or more classifications (e.g., categories, or types of labels) to obtain multi-label characters.

A system may include a processor configured to: obtain an image of an airport terminal chart; based on a latitudinal set of characters, determine a latitude for each line of latitude; based on the latitude for each line of latitude and a first image distance between the lines of latitude, determine a first ratio of latitudinal degrees between the lines of latitude to the first image distance; based on a longitudinal set of characters, determine a longitude for each line of longitude; based on the longitude for each line of longitude and a second image distance between the lines of longitude, determine a second ratio of longitudinal degrees between the lines of longitude to the second image distance; and output information associated with the first ratio, the second ratio, the determined latitude for each line of latitude, and the determined longitude for each line of longitude.

A system may include a processor configured to: obtain an image of an airport terminal chart; based on a latitudinal set of characters, determine a latitude for each line of latitude; based on the latitude for each line of latitude and a first image distance between the lines of latitude, determine a first ratio of latitudinal degrees between the lines of latitude to the first image distance; based on a longitudinal set of characters, determine a longitude for each line of longitude; based on the longitude for each line of longitude and a second image distance between the lines of longitude, determine a second ratio of longitudinal degrees between the lines of longitude to the second image distance; and output information associated with the first ratio, the second ratio, the determined latitude for each line of latitude, and the determined longitude for each line of longitude.

An inference method includes acquiring similarities between a domain name serving as an analysis object and each domain name indicated in a legitimate domain name list as feature amounts, and inferring a degree that the domain name serving as the analysis object is wrongly recognized as a legitimate domain name based on the feature amounts acquired at the acquiring and a training model that outputs, as a response to input of the feature amounts, a degree that the domain name serving as the analysis object is wrongly recognized as the legitimate domain name, by processing circuitry.

An inference method includes acquiring similarities between a domain name serving as an analysis object and each domain name indicated in a legitimate domain name list as feature amounts, and inferring a degree that the domain name serving as the analysis object is wrongly recognized as a legitimate domain name based on the feature amounts acquired at the acquiring and a training model that outputs, as a response to input of the feature amounts, a degree that the domain name serving as the analysis object is wrongly recognized as the legitimate domain name, by processing circuitry.

A method for detecting comparison persons 7 to a search person 4, wherein a plurality of classification persons 3 is classified by extracting values W1,W2,W3 for classification features K1,K2,K3 from classification images 2 of the classification persons 3, the classification being ambiguous in such a way that the classification does not enable a unique identification of any of the classification persons 3, wherein during a search for a search person 4 using a search image 5 by a comparison of values of search features from the search image 5 with values W1,W2,W3 of classification features K1,K2,K3, at least two classification persons 3 are output as comparison persons 7.

A method for detecting comparison persons 7 to a search person 4, wherein a plurality of classification persons 3 is classified by extracting values W1,W2,W3 for classification features K1,K2,K3 from classification images 2 of the classification persons 3, the classification being ambiguous in such a way that the classification does not enable a unique identification of any of the classification persons 3, wherein during a search for a search person 4 using a search image 5 by a comparison of values of search features from the search image 5 with values W1,W2,W3 of classification features K1,K2,K3, at least two classification persons 3 are output as comparison persons 7.