6 citations · 8 across the 3 of their papers we have counts for
7 papers
Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
Rajiv Sambasivan, Mark Burgess, Jörg Schad +4
Developing shopping experiences that delight the customer requires businesses to understand customer taste. This work reports a method to learn the shopping preferences of frequent…
A Bayesian Perspective of Statistical Machine Learning for Big Data
Rajiv Sambasivan, Sourish Das, Sujit K Sahu
Statistical Machine Learning (SML) refers to a body of algorithms and methods by which computers are allowed to discover important features of input data sets which are often very…
Big Data Classification Using Augmented Decision Trees
Rajiv Sambasivan, Sourish Das
We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as…
Big Data Regression Using Tree Based Segmentation
Rajiv Sambasivan, Sourish Das
Scaling regression to large datasets is a common problem in many application areas. We propose a two step approach to scaling regression to large datasets. Using a regression tree…
A Statistical Machine Learning Approach to Yield Curve Forecasting
Rajiv Sambasivan, Sourish Das
Yield curve forecasting is an important problem in finance. In this work we explore the use of Gaussian Processes in conjunction with a dynamic modeling strategy, much like the Kal…
Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data
Rajiv Sambasivan, Sourish Das
Datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. In this work we examine an approach to clustering such datase…
Fast Gaussian Process Regression for Big Data
Sourish Das, Sasanka Roy, Rajiv Sambasivan
Gaussian Processes are widely used for regression tasks. A known limitation in the application of Gaussian Processes to regression tasks is that the computation of the solution req…