Certificate Verification        Student Ambassador          Quick Pay        Request For Enquiry
Sell Your Project      Apply for franchise          
  • 0877-2261612       
  • +91-9030 333 433
  • +91-9966 062 884

Nonparametric Distributed Learning Architecture For Big Data: Algorithm And Applications

NONPARAMETRIC DISTRIBUTED LEARNING ARCHITECTURE FOR BIG DATA: ALGORITHM AND APPLICATIONS

  • Project Code :
  • TCREJA19_59
  • .
Buy Now

Cost will be available soon

Download Project Document / Synopsis

NONPARAMETRIC DISTRIBUTED LEARNING ARCHITECTURE FOR BIG DATA: ALGORITHM AND APPLICATIONS

Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications

Abstract

Dramatic increases in the size and complexity of modern datasets have made traditional “centralized” statistical inference prohibitive. In addition to computational challenges associated with big data learning, the presence of numerous data types (e.g. discrete, continuous, categorical, etc.) makes automation and scalability difficult. A question of immediate concern is how to design a data-intensive statistical inference architecture without changing the basic statistical modelling principles developed for “small” data over the last century. To address this problem, we present MetaLP, a flexible, distributed statistical modelling framework suitable for large-scale data analysis, where statistical inference meets big data computing. This framework consists of three key components that work together to provide a holistic solution for big data learning: (i) partitioning massive data into smaller datasets for parallel processing and efficient computation, (ii) modern nonparametric learning based on a specially designed, orthonormal data transformation leading to mixed data algorithms, and finally (iii) combining heterogeneous “local” inferences from partitioned data using meta-analysis techniques to arrive at the “global” inference for the original big data. We present an application of this general theory in the context of a nonparametric two-sample inference algorithm for Expedia personalized hotel recommendations based on 10 million search result records.

innovative
innovative Request Video

Package Features

  • 24/7 Support
  • Ticketing System
  • Voice Conference
  • Video On Demand
  • Remote Connectivity
  • Code Customization
  • Customization
  • Live Chat Support
  • Toll Free Support

Includes

  • Complete Source Code
  • Complete Documentation
  • Complete Presentation Slides
  • Flow Diagram
  • Database File
  • Screenshots
  • Execution Procedure
  • Readme File
  • Addons
  • Video Tutorials

Leave Your Comment!

Your email address will not be published. Required fields are marked *

Latest Projects

Call us : (+91) 9030333433 / 08772261612
Mail us : takeoffstudentprojects@gmail.com
Mail us : info@takeoffprojects.com