Results 1 
4 of
4
Learning Bayesian belief networks: An approach based on the MDL principle
 Computational Intelligence
, 1994
"... A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being lear ..."
Abstract

Cited by 247 (7 self)
 Add to MetaCart
(Show Context)
A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being learned. In particular, our method can learn unrestricted multiplyconnected belief networks. Furthermore, unlike other approaches our method allows us to tradeo accuracy and complexity in the learned model. This is important since if the learned model is very complex (highly connected) it can be conceptually and computationally intractable. In such a case it would be preferable to use a simpler model even if it is less accurate. The MDL principle o ers a reasoned method for making this tradeo. We also show that our method generalizes previous approaches based on Kullback crossentropy. Experiments have been conducted to demonstrate the feasibility of the approach. Keywords: Knowledge Acquisition � Bayes Nets � Uncertainty Reasoning. 1
An Optimal Approximation Algorithm For Bayesian Inference
 Artificial Intelligence
, 1997
"... Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence node E, is NPhard. This result holds for belief networks that are allowed to contain extreme conditional probabilitiesthat is, conditional probabilities arbitrarily close to 0. Nevertheless, all p ..."
Abstract

Cited by 54 (2 self)
 Add to MetaCart
(Show Context)
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence node E, is NPhard. This result holds for belief networks that are allowed to contain extreme conditional probabilitiesthat is, conditional probabilities arbitrarily close to 0. Nevertheless, all previous approximation algorithms have failed to approximate efficiently many inferences, even for belief networks without extreme conditional probabilities. We prove that we can approximate efficiently probabilistic inference in belief networks without extreme conditional probabilities. We construct a randomized approximation algorithmthe boundedvariance algorithmthat is a variant of the known likelihoodweighting algorithm. The boundedvariance algorithm is the first algorithm with provably fast inference approximation on all belief networks without extreme conditional probabilities. From the boundedvariance algorithm, we construct a deterministic approximation algorithm u...
Towards Perceptual Intelligence: Statistical Modeling of Human Individual and Interactive Behaviors
 Prediction of Human Behavior, IEEE Intelligent Vehicles
, 1995
"... This thesis presents a computational framework for the automatic recognition and prediction of different kinds of human behaviors from video cameras and other sensors, via perceptually intelligent systems that automatically sense and correctly classify human behaviors, by means of Machine Perception ..."
Abstract

Cited by 17 (6 self)
 Add to MetaCart
This thesis presents a computational framework for the automatic recognition and prediction of different kinds of human behaviors from video cameras and other sensors, via perceptually intelligent systems that automatically sense and correctly classify human behaviors, by means of Machine Perception and Machine Learning techniques. In the thesis I develop the statistical machine learning algorithms (dynamic graphical models) necessary for detecting and recognizing individual and interactive behaviors. In the case of the interactions two Hidden Markov Models (HMMs) are coupled in a novel architecture called Coupled Hidden Markov Models (CHMMs) that explicitly captures the interactions between them. The algorithms for learning the parameters from data as well as for doing inference with those models are developed and described. Four systems that experimentally evaluate the proposed paradigm are presented: (1) LAFTER, an automatic face detection and tracking system with facial expression recognition; (2) a TaiChi gesture recognition system; (3) a pedestrian surveillance system that recognizes typical human to human interactions; (4) and a SmartCar for driver maneuver recognition. These systems capture human behaviors of different nature and increasing complexity: first, isolated, singleuser facial expressions, then, twohand gestures and humantohuman interactions,...
Learning, Bayesian Probability, Graphical Models, and Abduction
 Abduction and Induction: Essays on their Relation and Integration, Chapter 10
, 1998
"... In this chapter I review Bayesian statistics as used for induction and relate it to logicbased abduction. Much reasoning under uncertainty, including induction, is based on Bayes' rule. Bayes' rule is interesting precisely because it provides a mechanism for abduction. I review work of Bu ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
(Show Context)
In this chapter I review Bayesian statistics as used for induction and relate it to logicbased abduction. Much reasoning under uncertainty, including induction, is based on Bayes' rule. Bayes' rule is interesting precisely because it provides a mechanism for abduction. I review work of Buntine that argues that much of the work on Bayesian learning can be best viewed in terms of graphical models such as Bayesian networks, and review previous work of Poole that relates Bayesian networks to logicbased abduction. This lets us see how much of the work on induction can be viewed in terms of logicbased abduction. I then explore what this means for extending logicbased abduction to richer representations, such as learning decision trees with probabilities at the leaves. Much of this paper is tutorial in nature; both the probabilistic and logicbased notions of abduction and induction are introduced and motivated. 1 Introduction This paper explores the relationship between learning (induct...