We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for a particular instance (e.g., user or patient) where the training data consists of passive observations of covariates, treatment, and the outcome of the treatment. The standard approach to this problem is regress and compare: split the training data b...

1 Introduction

A trajectory is defined as the record of time-varying spatial phenomenon. The trajectory database is an important research area that has received a lot of interest in the last decade, with the objective of trajectory databases being to extend existing database technology to support the representation and querying of moving objects and their trajectories. Querying in trajectory databases can be ...

We present a new approach to SAR image segmentation based on a Poisson approximation to the SAR amplitude image It has been established that SAR amplitude images are well approximated using Rayleigh distributions We show that with suitable modi cations we can model piecewise homogeneous regions such as tanks roads scrub etc within the SAR amplitude image using a Poisson model that bears a known...

The random sample consensus (RANSAC) algorithm is frequently used in computer vision to estimate the parameters of a signal in the presence of noisy and even spurious observations called gross errors. Instead of just one signal, we desire to estimate the parameters of multiple signals, where at each time step a set of observations of generated from the underlying signals and gross errors are re...

Grouping by graph partitioning is an effective engine for perceptual organization. This graph partitioning process, mainly motivated by computational efficiency considerations, is usually implemented as recursive bi-partitioning, where at each step the graph is broken into two parts based on a partitioning measure. We study four such measures, namely, the minimum cut [11], average cut [6], Shi-...

Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of exhaustive search procedures usually applied to fit such models have been known for a long time: Overfitting and a selection bias towards covariates with many possible splits or missing values. While pruning procedures are able to solve the overfitting problem, the variable selection bias still ...

Aim of this paper is to propose a nonparametric method for the rating of financial stocks, such as Mutual Funds, Corporates, Equities etc. To this purpose, we refer to a procedure based on the joint use of linear discriminant analysis and tree based recursive partitioning. This method is applied when dealing with large sets of within-groups correlated covariates in order to overcome the dimensi...