Rocchio relevance feedback and latent semantic indexing (LSI) are well-known extensions of the vector space model for information retrieval (IR). This paper analyzes the statistical relationship between these extensions. The analysis focuses on each method’s basis in least-squares optimization. Noting that LSI and Rocchio relevance feedback both alter the vector space model in a way that is in ...

This project aims to characterize and classify tweets that show users exposing HIV risk behaviour through their tweets on the social networking site Twitter. A labeled dataset obtained from doctors in UCSD’s Anti Viral Research Center (AVRC) was used as the dataset. To get a better understanding of the data collected and to build a good classification model, a series of exploratory data analysi...

Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms ar...

With the advance in remote sensing, various machine learning techniques could be applied to study variable relationships. Although prediction models obtained by using machine learning techniques are suitable for predictions, they do not explicitly provide means for determining input-output variable relevance. The relevance information is often of interest to scientists since relationships among...

The Relevance Vector Machine(RVM) is a widely accepted Bayesian model commonly used for regression and classification tasks. In this paper we propose a multikernel version of the RVM and present an alternative inference algorithm based on Fourier domain computation to solve this model for large scale problems, e.g. images. We then apply the proposed method to the object detection problem with p...

This article gives a basic introduction to the principles of Bayesian inference in a machine learning context, with an emphasis on the importance of marginalisation for dealing with uncertainty. We begin by illustrating concepts via a simple regression task before relating ideas to practical, contemporary, techniques with a description of ‘sparse Bayesian’ models and the ‘relevance vector machi...

This paper presents an extension of the relevance vector machine (RVM) algorithm to multivariate regression. This allows the application to the task of estimating the pose of an articulated object from a single camera. RVMs are used to learn a oneto-many mapping from image features to state space, thereby being able to handle pose ambiguity.

The conventional maximum likelihood linear regression (MLLR)-based adaptation algorithm employed to acoustic hidden Markov models (HMMs) is too restricted in linear regression to represent the details of mapping charateristics. To overcome this problem, we propose the relevance vector regression (RVR)-based model parameter adaptation technique. In this framework, the conventional technique is e...