Methods are taught for creating training data for a learning algorithm, training the learning algorithm with the training data and using the trained learning algorithm to suggest domain names to users. A domain name registrar may store activities of a user on a registrar website. Preferably, domain name searches, selected suggested domain names and domain names registered to the user are stored as the training data in a training database. The training data may be stored so that earlier activities act as inputs to the learning algorithm while later activities are the expected outputs of the learning algorithm. Once trained, the learning algorithm may receive activities of other users and suggest domain names to the other users based on their activities.

A method and a system are described for context based clustering of one or more objects. The method comprises receiving, by the object clustering system, receiving, by an object clustering system, an object clustering request for one or more objects associated with a plurality of contextual parameters, where the plurality of contextual parameters comprises one or more physical attributes and one or more non-physical attributes. It further includes tagging the one or more non-physical attributes respectively to the one or more physical attributes. It further includes identifying a common context from the one or more physical attributes associated with the one or more objects based on the tagging. It further includes mapping the one or more physical attributes to the one or more objects based on the common context. It then includes clustering the one or more objects based on the mapping.

A fairing skin repair method based on measured wing data includes fairing skin registration. Data set P1 through denoising and filtering wing point cloud data is reorganized to obtain a key point set P. A histogram feature descriptor in a normal direction of any key point in set P and a skin point cloud data Q is calculated. Euclidean distance between feature descriptors of two points is calculated through K-nearest neighbor algorithm, and points with high similarity are added into a set M. A clustering is performed on set M using a Hough voting algorithm to obtain a local point cloud set P′ in set P. The method includes fairing skin repair. The boundary line of the point frame is projected onto Q, and a distance between a projection line on the point cloud and the boundary line is calculated to obtain an amount of skin to be repaired.

Machine learning-based approaches are used to create instances or visualizations of content appearing within an object in an image. For example, a user may submit a request for a preview or visualization of content within an object or other media such as a glass crystal. A trained model can process the content to generate adjustment data or other data that can be used to control image blending operations. The adjustment data can be applied to the pixel values of the content to modify the content in order to enable a visualization of the content within an object. The image portion can be modified such that the object appears to “blend” with and appear within the object. Image transformation techniques can be used to project the modified content onto a representation of an object. Thereafter, a visualization or preview of the content within the representation of the object can be presented.

In one implementation, a method includes identifying a first content-dependent feature associated with a data sector. The method further includes determining a baseline data sector associated with the data sector. The method further includes determining, by a processing device, a content-dependent delta between the first content-dependent feature and a second content-dependent feature of the baseline data sector. The method further includes providing the content-dependent delta and an indicator to the baseline data sector for storage on a plurality of storage devices.

In response to running at least one testing phrase on a previously trained text classifier and identifying a separate predicted classification label based on a score calculated for each respective at least one testing phrase, a text classifier decomposes extracted features summed in the score into word-level scores for each word in the at least one testing phrase. The text classifier assigns a separate heatmap value to each of the word-level scores, each respective separate heatmap value reflecting a weight of each word-level score. The text classifier outputs the separate predicted classification label and each separate heatmap value reflecting the weight of each word-level score for defining a heatmap identifying the contribution of each word in the at least one testing phrase to the separate predicted classification label for facilitating client evaluation of text classification anomalies.

Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Systems, methods, and other techniques for extracting data from obituaries are provided. In some embodiments, an obituary containing a plurality of words is received. Using a machine learning model, an entity tag from a set of entity tags may be assigned to each of one or more words of the plurality of words. Each particular tag from the set of entity tags may include a relationship component and a category component. The relationship component may indicate a relationship between a particular word and the deceased individual. The category component may indicate a categorization of the particular word to a particular category from a set of categories. The extracted data may be stored in a genealogical database.

Embodiments of the present invention provide an improvement to conventional machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. The proposed invention involves generating synthetic data clusters to be stored and used for retraining the main model as well as other models. In addition, the invention includes using one or more (subset) of the synthetic data clusters to train or retrain machine learning models, developing and training machine learning models that are trained with emerging synthetic data clusters, and ensembling machine learning models trained with emerging synthetic data clusters.

A system and method for self-learning operation of gate paddles is disclosed. Opening and closing of the gate paddles requires timing and other settings to avoid injury and fare evasion. Self-learning allows a machine learning model to adapt to new data dynamically. The new data captured at a fare gate improves the machine learning model, which can be shared the other similar fare gates within a transit system so that learning disseminates.