Results 1 - 10 of 154

Learning and classification of complex dynamics

by Ben North, Andrew Blake, Michael Isard, Jens Rittscher - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2000
"... AbstractÐStandard, exact techniques based on likelihood maximization are available for learning Auto-Regressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via ..."
Abstract - Cited by 89 (2 self) - Add to MetaCart
dynamics are studied via visually observed juggling; plausible dynamical models have been found to emerge from the learning process, and accurate classification of motion has resulted. In practice, EM-C learning is computationally burdensome and the paper concludes with some discussion of computational

An input output HMM architecture.

by Yoshua Bengio , Paolo Frasconi - Adv Neural Inf Process Syst , 1995
"... Abstract We i n troduce a recurrent a r c hitecture having a modular structure and we formulate a training procedure based on the EM algorithm. The resulting model has similarities to hidden Markov models, but supports recurrent networks processing style and allows to exploit the supervised learnin ..."
Abstract - Cited by 126 (16 self) - Add to MetaCart
Abstract We i n troduce a recurrent a r c hitecture having a modular structure and we formulate a training procedure based on the EM algorithm. The resulting model has similarities to hidden Markov models, but supports recurrent networks processing style and allows to exploit the supervised

The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data

by Thomas Hofmann - In IJCAI’ 99 , 1999
"... This paper presents a novel statistical latent class model for text mining and interactive information access. The described learning architecture, called Cluster{Abstraction Model (CAM), is purely data driven and utilizes context-speci c word occurrence statistics. In an intertwined fashion, the CA ..."
Abstract - Cited by 63 (0 self) - Add to MetaCart
This paper presents a novel statistical latent class model for text mining and interactive information access. The described learning architecture, called Cluster{Abstraction Model (CAM), is purely data driven and utilizes context-speci c word occurrence statistics. In an intertwined fashion

Semi-supervised learning with penalized probabilistic clustering

by Zhengdong Lu, Todd K. Leen - In Advances in , 2005
"... While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments of ..."
Abstract - Cited by 50 (2 self) - Add to MetaCart
While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments

c ○ 2000 Kluwer Academic Publishers. Manufactured in The Netherlands. Dynamic Learning with the EM Algorithm for Neural Networks

by J. F. G. De Freitas, M. Niranjan, A. H. Gee , 1999
"... Abstract. In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights. For th ..."
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Abstract. In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights

Enhancement of Fuzzy Possibilistic C-Means Algorithm using EM Algorithm (EMFPCM)

by R. Shanthi
"... The major difficulties that arise in several fields, comprising pattern recognition, machine learning and statistics, is clustering. The basic data clustering problem might be defined as finding out groups in data or grouping related objects together. A cluster is a group of objects which are simila ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
between them. In order to provide better clustering approaches that fits for all applications and to improve the efficiency of data clustering, this paper proposes a effective clustering techniques called Enhancement of Fuzzy Possibilistic C-Means Algorithm using EM Algorithm (EMFPCM). Thus with the help

Switching Kalman Filters

by Kevin P. Murphy , 1998
"... We show how many different variants of Switching Kalman Filter models can be represented in a unified way, leading to a single, general-purpose inference algorithm. We then show how to find approximate Maximum Likelihood Estimates of the parameters using the EM algorithm, extending previous results ..."
Abstract - Cited by 67 (2 self) - Add to MetaCart
on learning using EM in the non-switching case [DRO93, GH96a] and in the switching, but fully observed, case [Ham90]. 1 Introduction Dynamical systems are often assumed to be linear and subject to Gaussian noise. This model, called the Linear Dynamical System (LDS) model, can be defined as x t = A t x t

EM for Perceptual Coding and Reinforcement Learning Tasks

by Yuri Ivanov, Bruce Blumberg, Alex Pentland - In Symposium on Intelligent Robotic Systems 2000 , 2000
"... The paper presents an algorithm for an EM-based reinforc% ent-drivenc lustering. As shown here it is applicV le to the reinforcq ent learning setting withc7 tinuous state/disck4 eac ion spacD E-step of the algorithm c mputes the posterior given the data and the reinforcNN4 t. Although designed to di ..."
Abstract - Cited by 6 (3 self) - Add to MetaCart
The paper presents an algorithm for an EM-based reinforc% ent-drivenc lustering. As shown here it is applicV le to the reinforcq ent learning setting withc7 tinuous state/disck4 eac ion spacD E-step of the algorithm c mputes the posterior given the data and the reinforcNN4 t. Although designed

Quality-Based Learning

by Freiburg Im Breisgau, Klemens Schnattinger, Klemens Schnattinger, Klemens Schnattinger, Udo Hahn, Udo Hahn, Udo Hahn , 1998
"... We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual ..."
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. Appeared in: ECAII'98 - Proceedings of the 13th Biennial European Conference on Artificial Intelligence/em¿, 23-28 August 1998, Brighton Centre, Brighton, UK.pp.160-164 c fl 1998 ECAI 98. 13th European Conference on Artificial Intelligence Edited by Henri Prade Published in 1998 by John Wiley &

Ersatz learning, inauthentic testing

by John F. Mcclymer, Lucia Z. Knoles , 1992
"... This Is a true story. A colleague of ours teaches an introductory calculus section. Early one term, he and his cEass were working through, some standard motion problems; 'A boy drops a water balloon from a window. If It takes 0.8 seconds to strike hts erstwhile friend, who is 5 feet tall, how h ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
This Is a true story. A colleague of ours teaches an introductory calculus section. Early one term, he and his cEass were working through, some standard motion problems; 'A boy drops a water balloon from a window. If It takes 0.8 seconds to strike hts erstwhile friend, who is 5 feet tall, how
Results 1 - 10 of 154