Results 1  10
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154
Learning and classification of complex dynamics
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... AbstractÐStandard, exact techniques based on likelihood maximization are available for learning AutoRegressive 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 ..."
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Cited by 89 (2 self)
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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, EMC learning is computationally burdensome and the paper concludes with some discussion of computational
An input output HMM architecture.
 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 ..."
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Cited by 126 (16 self)
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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 clusterabstraction model: Unsupervised learning of topic hierarchies from text data
 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 contextspeci c word occurrence statistics. In an intertwined fashion, the CA ..."
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Cited by 63 (0 self)
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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 contextspeci c word occurrence statistics. In an intertwined fashion
Semisupervised learning with penalized probabilistic clustering
 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 ..."
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Cited by 50 (2 self)
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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
, 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 Estep we apply a forwardbackward RauchTungStriebel 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 Estep we apply a forwardbackward RauchTungStriebel smoother to compute the network weights
Enhancement of Fuzzy Possibilistic CMeans Algorithm using EM Algorithm (EMFPCM)
"... 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 ..."
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Cited by 1 (0 self)
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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 CMeans Algorithm using EM Algorithm (EMFPCM). Thus with the help
Switching Kalman Filters
, 1998
"... We show how many different variants of Switching Kalman Filter models can be represented in a unified way, leading to a single, generalpurpose inference algorithm. We then show how to find approximate Maximum Likelihood Estimates of the parameters using the EM algorithm, extending previous results ..."
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Cited by 67 (2 self)
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on learning using EM in the nonswitching 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
 In Symposium on Intelligent Robotic Systems 2000
, 2000
"... The paper presents an algorithm for an EMbased reinforc% entdrivenc lustering. As shown here it is applicV le to the reinforcq ent learning setting withc7 tinuous state/disck4 eac ion spacD Estep of the algorithm c mputes the posterior given the data and the reinforcNN4 t. Although designed to di ..."
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Cited by 6 (3 self)
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The paper presents an algorithm for an EMbased reinforc% entdrivenc lustering. As shown here it is applicV le to the reinforcq ent learning setting withc7 tinuous state/disck4 eac ion spacD Estep of the algorithm c mputes the posterior given the data and the reinforcNN4 t. Although designed
QualityBased Learning
, 1998
"... We introduce a methodology for automating the maintenance of domainspecific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from realworld 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¿, 2328 August 1998, Brighton Centre, Brighton, UK.pp.160164 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
, 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 ..."
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Cited by 2 (0 self)
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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