Open AccessJournal Article
A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
Reads0
Chats0
TLDR
In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.Abstract:
We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.read more
Citations
More filters
Proceedings ArticleDOI
An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation
TL;DR: A novel cognition-enabled control framework enables a robotic assistant to enrich its own experience by acquisition of human task knowledge during joint manipulation using hierarchically clustered Hidden Markov Models on a mobile bi-manual platform.
Journal ArticleDOI
Ambiguously Labeled Learning Using Dictionaries
TL;DR: Extensive evaluations on four unconstrained face recognition datasets demonstrate that the proposed dictionary-based learning method performs significantly better than state-of-the-art ambiguously labeled learning approaches.
Proceedings ArticleDOI
Stream prediction using a generative model based on frequent episodes in event sequences
TL;DR: A new algorithm for sequence prediction over long categorical event streams that is suitable for predicting targeted user-behaviors from large volumes of anonymous search session interaction logs from a commercially-deployed web browser tool-bar is presented.
Journal IssueDOI
Caroline: An autonomously driving vehicle for urban environments
Fred W. Rauskolb,Kai Berger,Christian Lipski,Marcus Magnor,Karsten Cornelsen,Jan Effertz,Thomas Form,Fabian Graefe,Sebastian Ohl,Walter Schumacher,Jörn Marten Wille,Peter Hecker,Tobias Nothdurft,Michael Doering,Kai Homeier,Johannes Morgenroth,Lars Wolf,Christian Basarke,Christian Berger,Tim Gülke,Felix Klose,Bernhard Rumpe +21 more
TL;DR: The 2007 DARPA Urban Challenge afforded the golden opportunity for the Technische Universitat Braunschweig to demonstrate its abilities to develop an autonomously driving vehicle to compete with the world's best, and team CarOLO qualified early for the DARPA urban Challenge Final Event and was among only 11 teams from initially 89 competitors to compete in the final.
Journal ArticleDOI
Feature vector classification based speech emotion recognition for service robots
TL;DR: On SER experiments using an emotional speech corpus, the proposed classification approach exhibited superior performance to conventional methods, and displayed an almost human-level performance, achieved commercially applicable performance for two-class (negative vs. non-negative) emotion recognition.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
The Fractal Geometry of Nature
TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.