Abstract: Disclosures of the present invention relate to a system and method of intelligently matching players intending to play a multiplayer online game. The system in the present invention stores data of players which is then used to compare between players and select matches. The data corresponds to player's statistical parameters as well as historical and demographic data. Further, the system is configured to dynamically adjust matchmaking objectives by modifying the parameters to select and the weightage for individual parameters. Hence, the improved system and method is capable of matching players in games that are challenging, engaging as well as fun to play for the players.

Abstract: Disclosed herein are platforms, systems, devices, software, and methods for processing and analyzing real-time data to generate predictions of near future events. Machine learning algorithms can be configured for varying levels of aggression to enhance timeliness of the predictions. Ensemble machine learning techniques combining a plurality of trained models configured with higher and lower aggression levels can be used to improve both timeliness and accuracy.

Abstract: Computer implemented system and method for classifying users is provided. The method includes obtaining a data set from one or more sources corresponding to one or more users; segmenting the data set into segments, each segment comprising one or more data points, wherein each data point is specific and unique to a user; identifying difficult regions from one or more segments; computing a standard deviation of distances between the data points within each of the identified difficult regions for each of the identified difficult regions; selecting a radial basis function (RBF) kernel based on the standard deviation computed for each of the identified difficult regions; generating a support vector machine model on each of the identified difficult regions based on the selected RBF kernel to obtain a hyper plane comprising support vectors and weight assigned to the vectors; and classifying the users into a category based on the hyper plane.

Abstract: Computer implemented system and method for classifying users is provided. The method includes obtaining a data set from one or more sources corresponding to one or more users; segmenting the data set into segments, each segment comprising one or more data points, wherein each data point is specific and unique to a user; identifying difficult regions from one or more segments; computing a standard deviation of distances between the data points within each of the identified difficult regions for each of the identified difficult regions; selecting a radial basis function (RBF) kernel based on the standard deviation computed for each of the identified difficult regions; generating a support vector machine model on each of the identified difficult regions based on the selected RBF kernel to obtain a hyper plane comprising support vectors and weight assigned to the vectors; and classifying the users into a category based on the hyper plane.