Neighborhood-level learning techniques for nonparametric scene models

Representation Models and Machine Learning Techniques for Scene Classificatio

Scene classification is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Humans are able to recognize complex visual scenes at a single glance, despite the number of objects with different poses, colors, shadows and textures that may be contained in the scenes. Understanding the robustness and rapidness of this human ability has been a fo...

Bayesian Learning Techniques for Nonparametric Identification

Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction

Robots operating in domestic environments need to deal with a variety of different objects. Often, these objects are neither placed randomly, nor independently of each other. For example, objects on a breakfast table such as plates, knives, or bowls typically occur in recurrent configurations. In this paper, we propose a novel hierarchical generative model to reason about latent object constell...

Nonparametric Bayesian Models for Unsupervised Learning

NONPARAMETRIC BAYESIAN MODELS FOR UNSUPERVISED LEARNING Pu Wang, PhD George Mason University, 2011 Dissertation Director: Carlotta Domeniconi Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for data analysis and mining. Unfortunately, clustering is an ill-posed problem and, as...

Nonparametric Bayesian Models for Machine Learning

Nonparametric Bayesian Models for Machine Learning by Romain Jean Thibaux Doctor of Philosophy in Computer Science and the Designated Emphasis in Communication, Computation and Statistics University of California, Berkeley Professor Michael I. Jordan, Chair This thesis presents general techiques for inference in various nonparametric Bayesian models, furthers our understanding of the stochastic...