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 m≥3, and m>n≥2; 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 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 m≥3, and m>n≥2; 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.

A quantization parameter optimization method includes: determining a cost function in which a regularization term is added to an error function, the regularization term being a function of a quantization error that is an error between a weight parameter of a neural network and a quantization parameter that is a quantized weight parameter; updating the quantization parameter by use of the cost function; and determining, as an optimized quantization parameter of a quantization neural network, the quantization parameter with which a function value derived from the cost function satisfies a predetermined condition, the optimized quantization parameter being obtained as a result of repeating the updating, the quantization neural network being the neural network, the weight parameter of which has been quantized, wherein the function value derived from the regularization term and an inference accuracy of the quantization neural network are negatively correlated.

A quantization parameter optimization method includes: determining a cost function in which a regularization term is added to an error function, the regularization term being a function of a quantization error that is an error between a weight parameter of a neural network and a quantization parameter that is a quantized weight parameter; updating the quantization parameter by use of the cost function; and determining, as an optimized quantization parameter of a quantization neural network, the quantization parameter with which a function value derived from the cost function satisfies a predetermined condition, the optimized quantization parameter being obtained as a result of repeating the updating, the quantization neural network being the neural network, the weight parameter of which has been quantized, wherein the function value derived from the regularization term and an inference accuracy of the quantization neural network are negatively correlated.

A method for brand recognition in video by implementing a brand recognition application coupled to a streaming media player, for identifying an observed set of brands streamed in a broadcast video; receiving, by the brand recognition application, a broadcast video stream of a series of images contained in consecutive frames about an object of interest; extracting a set of brand features from each of image received by applying a trained brand recognition model with neural networks in order to detect one or more features related to each displayed object of interest in each frame, wherein the object of interest is associated with a brand image contained video content displayed to a viewer; and displaying, by a graphic user interface, information from the brand recognition application comprising at least time detected of the brand image in the video content of the broadcast video.

A method for brand recognition in video by implementing a brand recognition application coupled to a streaming media player, for identifying an observed set of brands streamed in a broadcast video; receiving, by the brand recognition application, a broadcast video stream of a series of images contained in consecutive frames about an object of interest; extracting a set of brand features from each of image received by applying a trained brand recognition model with neural networks in order to detect one or more features related to each displayed object of interest in each frame, wherein the object of interest is associated with a brand image contained video content displayed to a viewer; and displaying, by a graphic user interface, information from the brand recognition application comprising at least time detected of the brand image in the video content of the broadcast video.

A computer-implemented method for refining dataset to accurately represent output of an artificial intelligence model includes generating a plurality of data points used to interpret a decision of an artificial intelligence model. A subset of data points from the generated plurality of data points satisfying one or more constraints is identified. A linear model is applied on the identified subset of data points satisfying the one or more constraints. One or more insights illustrating the decision of the artificial intelligence model is generated.

A computer-implemented method for refining dataset to accurately represent output of an artificial intelligence model includes generating a plurality of data points used to interpret a decision of an artificial intelligence model. A subset of data points from the generated plurality of data points satisfying one or more constraints is identified. A linear model is applied on the identified subset of data points satisfying the one or more constraints. One or more insights illustrating the decision of the artificial intelligence model is generated.

A recording medium storing an explanatory program for causing a computer to execute an explanatory process. The process includes: generating a plurality of pieces of data based on first data; calculating a ratio of output results, among a plurality of results output in a case that each of the plurality of pieces of data is input to a machine learning model, different from first results output in a case that the first data is input to the machine learning model; generating a linear model based on the plurality of pieces of data and the plurality of results in a case that the calculated ratio satisfies a criterion; and outputting explanatory information with respect to the first results based on the linear model.

A method for brand recognition in video by implementing a brand recognition application coupled to a streaming media player, for identifying an observed set of brands streamed in a broadcast video; receiving, by the brand recognition application, a broadcast video stream of a series of images contained in consecutive frames about an object of interest; extracting a set of brand features from each of image received by applying a trained brand recognition model with neural networks in order to detect one or more features related to each displayed object of interest in each frame, wherein the object of interest is associated with a brand image contained video content displayed to a viewer; and displaying, by a graphic user interface, information from the brand recognition application comprising at least time detected of the brand image in the video content of the broadcast video.