A system, method and apparatus for detecting facial expressions according to EMG signals.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

A system is provided for generating a classifier for classifying electromagnetic data (e.g., ECG) derived from an electromagnetic source (e.g., heart). The system accesses a computational model of the electromagnetic source. The computational model models the electromagnetic output of the electromagnetic source over time based on a source configuration (e.g., rotor location) of the electromagnetic source. The system generates, for each different source configuration (e.g., different rotor locations), a modeled electromagnetic output (e.g., ECG) of the electromagnetic source for that source configuration. For each modeled electromagnetic output, the system derives the electromagnetic data for the modeled electromagnetic output and generates a label (e.g., rotor location) for the derived electromagnetic data from the source configuration for the modeled electromagnetic data. The system trains a classifier with the derived electromagnetic data and the labels as training data. The classifier can then be used to classify the electromagnetic output collected from patients.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

Provided is a coefficient calculation device and the like that can promptly calculate accurate extinction coefficient for a certain area. The coefficient calculation device is configured to calculate an extinction coefficient for a part of partial areas among a plurality of partial areas in a field in accordance with a predetermined calculation processing, generate relevance information representing a relevance between the extinction coefficient calculated for the part of partial areas and a characteristic value representing a character of the part of partial areas, and calculate an extinction coefficient for another partial area different from the part of partial areas among the plurality of partial areas based on the characteristic value for the another partial area and the generated relevance information.

Methods are provided for determining discriminant functions of minimum risk quadratic classification systems, wherein a discriminant function is represented by a geometric locus of a principal eigenaxis of a quadratic decision boundary. A geometric locus of a principal eigenaxis is determined by solving a system of fundamental locus equations of binary classification, subject to geometric and statistical conditions for a minimum risk quadratic classification system in statistical equilibrium. Feature vectors and machine learning algorithms are used to determine discriminant functions and ensembles of discriminant functions of minimum risk quadratic classification systems, wherein a discriminant function of a minimum risk quadratic classification system exhibits the minimum probability of error for classifying given collections of feature vectors and unknown feature vectors related to the collections.

The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m3, and m>n2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

One or more systems and/or methods for providing content to a user are provided. An image, associated with a user, may be evaluated utilizing an image classifier to identify an object within the image. The object may be utilized to identify a predicted class for the user. In an example, the predicted class may correspond to a life event (e.g., graduating college, having a baby, buying a house, etc.) and/or a life stage (e.g., adolescence, retirement, etc.). Locational information (e.g., a geotag) for the image may be evaluated to determine an image location (e.g., a location where the image was generated). Responsive to the image location corresponding to a home location of the user, the predicted class may be determined to be a class associated with the user. Content (e.g., promotional content) may be selected from a content repository based upon the class and subsequently provided to the user.

Methods are provided for determining discriminant functions of minimum risk quadratic classification systems, wherein a discriminant function is represented by a geometric locus of a principal eigenaxis of a quadratic decision boundary. A geometric locus of a principal eigenaxis is determined by solving a system of fundamental locus equations of binary classification, subject to geometric and statistical conditions for a minimum risk quadratic classification system in statistical equilibrium. Feature vectors and machine learning algorithms are used to determine discriminant functions and ensembles of discriminant functions of minimum risk quadratic classification systems, wherein a discriminant function of a minimum risk quadratic classification system exhibits the minimum probability of error for classifying given collections of feature vectors and unknown feature vectors related to the collections.