Future Wei Technologies, Inc.

Huawei Technologies Co., Limited

Jiangsheng Yu

Hui Zang

Xiaocun Que

Shijun Ma

A machine-learning classification system includes a first machine-learning classifier that classifies each element of a plurality of data items to generate a plurality of classified data items. A second machine-learning classifier identifies misclassified elements of the plurality of classified data items and reclassifies each of the identified misclassified elements to generate a plurality of reclassified data items. A second machine-learning classifier identifies unclassified elements of the plurality of classified data items and classifies each of the identified unclassified elements to generate a plurality of reclassified data items. An ensemble classifier adjusts the classifications of the elements of the plurality of classified data items in response to the plurality of reclassified data items and the plurality of newly-classified elements.

Boosting AI identification learning

A method for loading boxes in containers is provided. Characteristics of a set of containers having substantially equal dimensions are obtained. Characteristics of a plurality of boxes to be packed into one or more of the set of containers are obtained. A set of layer descriptors is created, each layer descriptor including a layer thickness, identifiers of boxes assigned to the layer, and a description of an arrangement of the boxes in the layer. Each of the boxes is assigned to a single layer descriptor. An optimized assignment of the layer descriptors to containers is determined. An ordered list of box loading instructions for each container is generated. The boxes are loaded in their assigned containers according to the ordered list for the container.

Container packing system

A system and method of automatically assigning a priority rank to messages. The system and method accesses a message data store and assigns a priority rank to each message. The priority rank is selected from a priority rank scale by, for each message, parsing the message for features present in the message and calculating a predicted intensity score for the message using a user-specific classifier. The classifier is trained from user training data which includes prior user messages on which a machine learning algorithm operates. The training data is labeled by scores calculated based on the actual activates performed by the user to each message. The priority rank of each message can be used to improve message processing in message processing systems.

Personalized message priority classification

A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers.

Stereoscopic learning for classification

The disclosure relates to technology for generating a data set comprising random numbers that are distributed by a multivariate population distribution. A set of empirical cumulative distribution functions are constructed from a collection of multidimensional random samples of the multivariate population, where each empirical cumulative distribution function is constructed from observations of a random variable. A number of multidimensional sample points are sampled from the collection of multidimensional random samples and the number of multidimensional sample points are each replaced with random neighbors to generate cloned data.

Sample-based multidimensional data cloning

A method includes obtaining via a programmed computer, a first set of n random samples and a second set of n+k random samples from a base set of samples where k is a lag, iteratively adding more random samples to the first and second sets from the base set via the programmed computer, obtaining a distance between the first and second sets of random samples by calculating via the programmed computer, an empirical cumulative distribution function (ECDF) for the first and second sets in each iteration until the distance between the ECDFs is below a threshold, and constructing a stable empirical distribution representation via the programmed computer using a number of samples that is a function of the first and second sets whose distance is below the threshold.

Sample size estimator

The disclosure relates to technology for generating random numbers that are distributed by the population distribution. An empirical cumulative distribution function is constructed from random samples of the population, and a first random number is generated that is uniformly distributed over a first interval. A second interval in the empirical cumulative distribution function is found such that a range of values of the second interval cover the first random number. A second random number is then generated as an approximation to the random number drawn from the population, where the second random number is employed as part of a testing process.

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San Jose, CA, United States of America

Jiangsheng Yu