71 results about "Generalized linear model" patented technology

Circuit behaviors are scaled to different operating conditions by using a generalized nonlinear model. Nonlinear transforms are applied to the operating conditions and / or to the circuit behaviors contained in a library set. The transformed quantities have a more linear relationship between them. Parameters for the linear relationship are estimated based on the data and operating conditions in the library set. These parameters and nonlinear transforms can then be used to scale circuit behaviors to operating points not contained in the library set.

The invention relates to systems and methods for prioritizing video slices of H.264 video bitstream comprising: a memory storage and a processing unit coupled to the memory storage, wherein the processing unit operates to execute a low complexity scheme to predict the expected cumulative mean squared error (CMSE) contributed by the loss of a slice of H.264 video bitstream, wherein the processing unit operates to execute a series of actions comprising assigning each slice a predicted value according to the low complexity scheme; extracting video parameters during encoding process, said video parameters; and using a generalized linear model to model CMSE as a linear combination of the video parameters, wherein the video parameters are derived from analytical estimations by using a Generalized Linear Model (GLM) over a video database, encompassing videos of different characteristics such as high and low motion, camera panning, zooming and still videos, further comprising wherein the GLM is constructed in a training phase as follows: determining the distribution of the computed CMSE to be a Normal distribution with the Identity link function; sequentially adding covariates using the forward selection technique where by the best model is evaluated at each stage using the Akaike's Information Criterion (AIC); the training phase of the model generates regression coefficients; the final model is validated through the testing phase by predicting the CMSE for different video sequences, not in the training database; and by using the regression coefficients, the expected CMSE values are predicted for each slice.

Use of Generalized Context Trees to assign a unique state from a finite set to any string is provided. The method optionally refines the generalized context tree into a refined generalized context tree having a finite state machine (FSM) property. Refining occurs whenever the generalized context tree does not have the finite state machine property. Alternately, a method for constructing a representation of a source usable within an FSM is provided, comprising evaluating a node comprising a suffix tail and verifying the suffix tail is included in the representation, and inserting at least one node to the representation when the suffix tail is not in the representation.

A method and apparatus for creating a generalized response model for a sheet forming machine are provided. Sheet property profiles are measured while the setpoint of an actuator is changed. A response (or change) profile of the sheet property resulting from a setpoint change is calculated. A finite set of critical points are selected from the property response profile. Using the selected critical points, the property response profile is classified in one of a finite number of response types. Under each of the response types, the property response profile is fitted with a plurality of continuous functions associated therewith. These continuous functions are combined to form the response model that minimizes the deviation between the property response and the fitted combination of continuous functions.

A quantification system is configured for debiasing media creative efficiency. In some embodiments, the quantification system leverages a weighted generalized linear model (GLM) to determine the individual impacts of media creatives beyond network effects. To prepare input data for fitting the weighted GLM, the quantification system analyzes spot airing data, creates a specific data structure for storing observations (e.g., network-media creative combinations) that can be provided to the weight GLM as input, and computes additional input data points needed by the weighted GLM (e.g., network spend, media creative efficiency per network-media creative combination, etc.). The weighted GLM is then fitted to obtain coefficients representing the individual impacts of the media creatives. The quantification system utilizes the computed impacts to adjust the previously computed media creative efficiency for each media creative. In this way, relative performance of media creatives can be objectively quantified across networks without needing digital evidence.