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423
Sequential Monte Carlo Methods for Dynamic Systems
 Journal of the American Statistical Association
, 1998
"... A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ..."
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Cited by 573 (9 self)
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A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We deliver a guideline on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of RaoBlackwellization to improve performances. Examples from econometrics and engineering are presented to demonstrate the importance of RaoBlackwellization and to compare different Monte Carlo procedures. Keywords: Blind deconvolution; Bootstrap filter; Gibbs sampling; Hidden Markov model; Kalman filter; Markov...
An Algorithm that Learns What's in a Name
, 1999
"... In this paper, we present IdentiFinder^TM, a hidden Markov model that learns to recognize and classify names, dates, times, and numerical quantities. We have evaluated the model in English (based on data from the Sixth and Seventh Message Understanding Conferences [MUC6, MUC7] and broadcast news) ..."
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Cited by 351 (6 self)
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In this paper, we present IdentiFinder^TM, a hidden Markov model that learns to recognize and classify names, dates, times, and numerical quantities. We have evaluated the model in English (based on data from the Sixth and Seventh Message Understanding Conferences [MUC6, MUC7] and broadcast news) and in Spanish (based on data distributed through the First Multilingual Entity Task [MET1]), and on speech input (based on broadcast news). We report results here on standard materials only to quantify performance on data available to the community, namely, MUC6 and MET1. Results have been consistently better than reported by any other learning algorithm. IdentiFinder's performance is competitive with approaches based on handcrafted rules on mixed case text and superior on text where case information is not available. We also present a controlled experiment showing the effect of training set size on performance, demonstrating that as little as 100,000 words of training data is adequate to get performance around 90% on newswire. Although we present our understanding of why this algorithm performs so well on this class of problems, we believe that significant improvement in performance may still be possible.
Fast and Accurate Sentence Alignment of Bilingual Corpora
 In Stephen D
, 2002
"... Abstract. We present a new method for aligning sentences with their translations in a parallel bilingual corpus. Previous approaches have generally been based either on sentence length or word correspondences. Sentencelengthbased methods are relatively fast and fairly accurate. Wordcorrespondence ..."
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Cited by 99 (1 self)
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Abstract. We present a new method for aligning sentences with their translations in a parallel bilingual corpus. Previous approaches have generally been based either on sentence length or word correspondences. Sentencelengthbased methods are relatively fast and fairly accurate. Wordcorrespondencebased methods are generally more accurate but much slower, and usually depend on cognates or a bilingual lexicon. Our method adapts and combines these approaches, achieving high accuracy at a modest computational cost, and requiring no knowledge of the languages or the corpus beyond division into words and sentences. 1
Heterogeneous Learning in the Doppelgänger User Modeling System
 Interaction
, 1995
"... Doppelg anger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelg anger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to ..."
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Cited by 89 (0 self)
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Doppelg anger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelg anger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates on Doppelg anger's learning techniques and their implementation in an applicationindependent, sensorindependent environment. Key words: User model, machine learning, serverclient architecture, multivariate statistical analysis, Markov models, Beta distribution, linear prediction. 1 Introduction When users interact with a computer, they provide a great deal of information about themselves. Even when they are not physically at a computer console,...
Parameter Estimation in Stochastic Logic Programs
 Machine Learning
, 2000
"... . Stochastic logic programs (SLPs) are logic programs with labelled clauses which dene a loglinear distribution over refutations of goals. The loglinear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions ..."
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Cited by 78 (5 self)
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. Stochastic logic programs (SLPs) are logic programs with labelled clauses which dene a loglinear distribution over refutations of goals. The loglinear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions. We analyse the fundamental statistical properties of SLPs addressing issues concerning innite derivations, `unnormalised' SLPs and impure SLPs. After detailing existing approaches to parameter estimation for loglinear models and their application to SLPs, we present a new algorithm called failureadjusted maximisation (FAM). FAM is an instance of the EM algorithm that applies specically to normalised SLPs and provides a closedform for computing parameter updates within an iterative maximisation approach. We empirically show that FAM works on some small examples and discuss methods for applying it to bigger problems. c 2000 Kluwer Academic Publishers. Printed in the Netherlands. ...
Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics
, 1996
"... We present a method for the unsupervised segmentation of data streams originating from different unknown sources which alternate in time. We use an architecture consisting of competing neural networks. Memory is included in order to resolve ambiguities of inputoutput relations. In order to obtain m ..."
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Cited by 74 (22 self)
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We present a method for the unsupervised segmentation of data streams originating from different unknown sources which alternate in time. We use an architecture consisting of competing neural networks. Memory is included in order to resolve ambiguities of inputoutput relations. In order to obtain maximal specialization, the competition is adiabatically increased during training. Our method achieves almost perfect identification and segmentation in the case of switching chaotic dynamics where input manifolds overlap and inputoutput relations are ambiguous. Only a small dataset is needed for the training proceedure. Applications to time series from complex systems demonstrate the potential relevance of our approach for time series analysis and shortterm prediction. 1 Introduction Neural networks provide frameworks for the representation of relations present in data. Especially in the fields of classification and time series prediction, neural networks Corresponding author, email:k...
Learning Variable Length Markov Models of Behaviour
 Computer Vision and Image Understanding
, 2001
"... In recent years therehasbeen an increasedinterest in the modelling and recognition of human activities involving highly structured and semantically rich behaviour such as dance, aerobics, and sign language. A novel approachispresented for automatically acquiring stochastic models of the highlevel s ..."
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Cited by 71 (4 self)
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In recent years therehasbeen an increasedinterest in the modelling and recognition of human activities involving highly structured and semantically rich behaviour such as dance, aerobics, and sign language. A novel approachispresented for automatically acquiring stochastic models of the highlevel structureof an activity without the assumption of any prior knowledge. The process involves temporal segmentation into plausible atomic behaviour components and the use of variable length Markov models for the efficient representation of behaviours. Experimental results arepresented which demonstrate the synthesis of realistic sample behaviours and the performanceofmodels for longterm temporal prediction. Keywords: modelling behaviour, behaviour prediction, behaviour synthesis, variable length Markov models, Markov models, Ngrams, hidden Markov models, probabilistic finite state automata, statistical grammars, computer animation. 2 1
ContentBased Video Indexing Of TV Broadcast News Using Hidden Markov Models
, 1999
"... This paper presents a new approach to contentbased video indexing using Hidden Markov Models (HMMs). In this approach one feature vector is calculated for each image of the video sequence. These feature vectors are modeled and classified using HMMs. This approach has many advantages compared to oth ..."
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Cited by 63 (4 self)
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This paper presents a new approach to contentbased video indexing using Hidden Markov Models (HMMs). In this approach one feature vector is calculated for each image of the video sequence. These feature vectors are modeled and classified using HMMs. This approach has many advantages compared to other video indexing approaches. The system has automatic learning capabilities. It is trained by presenting manually indexed video sequences. To improve the system we use a video model, that allows the classification of complex video sequences. The presented approach works three times faster than realtime. We tested our system on TV broadcast news. The rate of 97.3 % correctly classified frames shows the efficiency of our system.
Fault detection and diagnosis in distributed systems: an approach by partially stochastic Petri nets
 special issue on Hybrid Systems
, 1998
"... We address the problem of alarm correlation in large distributed systems. The key idea is to make use of the concurrence of events in order to separate and simplify the state estimation in a faulty network. Petri nets and their causality semantics are used to model concurrency. Special partially ..."
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Cited by 62 (10 self)
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We address the problem of alarm correlation in large distributed systems. The key idea is to make use of the concurrence of events in order to separate and simplify the state estimation in a faulty network. Petri nets and their causality semantics are used to model concurrency. Special partially stochastic Petri nets are developed, that establish some kind of equivalence between concurrence and independence. The diagnosis problem is defined as the computation of the most likely history of the net given a sequence of observed alarms. Solutions are provided in four contexts, with a gradual complexity on the structure of observations.