Results 1 
9 of
9
Stochastic grammatical inference with Multinomial Tests
, 2002
"... We present a new statistical framework for stochastic grammatical inference algorithms based on a state merging strategy. We propose to use multinomial statistical tests to decide which states should be merged. This approach has three main advantages. First, since it is not based on asymptotic resul ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
We present a new statistical framework for stochastic grammatical inference algorithms based on a state merging strategy. We propose to use multinomial statistical tests to decide which states should be merged. This approach has three main advantages. First, since it is not based on asymptotic results, small sample case can be specifically dealt with. Second, all the probabilities associated to a state are included in a single test so that statistical evidence is cumulated. Third, a statistical score is associated to each possible merging operation and can be used for bestfirst strategy. Improvement over classical stochastic grammatical inference algorithm is shown on artificial data.
Probabilistic DFA Inference using KullbackLeibler Divergence and Minimality
 In Seventeenth International Conference on Machine Learning
, 2000
"... Probabilistic DFA inference is the problem of inducing a stochastic regular grammar from a positive sample of an unknown language. The ALERGIA algorithm is one of the most successful approaches to this problem. In the present work we review this algorithm and explain why its generalization criterion ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
Probabilistic DFA inference is the problem of inducing a stochastic regular grammar from a positive sample of an unknown language. The ALERGIA algorithm is one of the most successful approaches to this problem. In the present work we review this algorithm and explain why its generalization criterion, a state merging operation, is purely local. This characteristic leads to the conclusion that there is no explicit way to bound the divergence between the distribution de ned by the solution and the training set distribution (that is, to control globally the generalization from the training sample). In this paper we present an alternative approach, the MDI algorithm, in which the solution is a probabilistic automaton that trades o minimal divergence from the training sample and minimal size. An e cient computation of the KullbackLeibler divergence between two probabilistic DFAs is described, from which the new learning criterion is derived. Empirical results in the d...
Using Symbol Clustering to Improve Probabilistic Automaton Inference
, 1998
"... . In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERG ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
. In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERGIA algorithm combined with an independent clustering technique to the Air Travel Information System (ATIS) task. A 25 % reduction in perplexity was obtained. This result outperforms a trigram model under the same simple smoothing scheme. 1 Introduction Inference of deterministic finite automaton (DFA) from positive and negative data can be solved by the RPNI algorithm, proposed independently by Trakhtenbrot et al. [16] and by Oncina et al. [13]. This algorithm was used by Lang in his extensive experimental study of learning random deterministic automata from sparse samples [10]. An adapted version of this algorithm proved to be successful in the recent Abbadingo competition [9]. However th...
Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference
 10th European Conference on Machine Learning. Number 2837 in LNAI, SpringerVerlag (2003) 169–1180
, 2003
"... In this paper we study the influence of noise in probabilistic grammatical inference. We paradoxically bring out the idea that specialized automata deal better with noisy data than more general ones. We propose then to replace the statistical test of the Alergia algorithm by a more restrictive m ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
In this paper we study the influence of noise in probabilistic grammatical inference. We paradoxically bring out the idea that specialized automata deal better with noisy data than more general ones. We propose then to replace the statistical test of the Alergia algorithm by a more restrictive merging rule based on a test of proportion comparison.
Predictive Robot Programming
 In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2002
"... One of the main barriers to automating a particular task with a robot is the amount of time needed to program the robot. Decreasing the programming time would facilitate automation in domains previously o limits. In this paper, we present a novel method for leveraging the previous work of a user to ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
One of the main barriers to automating a particular task with a robot is the amount of time needed to program the robot. Decreasing the programming time would facilitate automation in domains previously o limits. In this paper, we present a novel method for leveraging the previous work of a user to decrease future programming time: predictive robot programming. The decrease in programming time is accomplished by predicting waypoints in future robot programs and automatically moving the manipulator endeector to the predicted position. To this end, we have developed algorithms that construct simple continuousdensity hidden Markov models by a statemerging algorithm based on waypoints from prior robot programs. We then use these models to predict the waypoints in future robot programs. While the focus of this paper is the application of predictive robot programming, we also give an overview of the underlying algorithms used and present experimental results.
On Unsupervised Learning of Mixtures of Markov Sources
, 2001
"... Unsupervised classification, or clustering, is one of the basic problems in data analysis. While the problem of unsupervised classification of independent random variables has been deeply investigated, the problem of unsupervised classification of dependent random variables, and in particular the pr ..."
Abstract
 Add to MetaCart
Unsupervised classification, or clustering, is one of the basic problems in data analysis. While the problem of unsupervised classification of independent random variables has been deeply investigated, the problem of unsupervised classification of dependent random variables, and in particular the problem of segmentation of mixtures of Markov sources, has been hardly addressed. At the same time supervised classification of Markov sources has become a fundamental problem with many important applications, such as analysis of texts, handwriting and speech, neural spike trains and biomolecular sequences. This question, previously approached with hidden Markov models (HMMs), in the last decade found additional interesting solutions using adaptive statistical models with improved learnability properties. One of such models is Prediction Suffix Tree (PST), suggested in [RST96]. Our current
Stochastic Grammatical Inference of Text . . .
 MACHINE LEARNING
, 2000
"... For a document collection in which structural elements are identified with markup, it is often necessary to construct a grammar retrospectively that constrains element nesting and ordering. This has been addressed by others as an application of grammatical inference. We describe an approach based on ..."
Abstract
 Add to MetaCart
For a document collection in which structural elements are identified with markup, it is often necessary to construct a grammar retrospectively that constrains element nesting and ordering. This has been addressed by others as an application of grammatical inference. We describe an approach based on stochastic grammatical inference which scales more naturally to large data sets and produces models with richer semantics. We adopt an algorithm that produces stochastic finite automata and describe modifications that enable better interactive control of results. Our experimental evaluation uses four document collections with varying structure.
Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms ∗
, 2009
"... In this paper, we aim at correcting distributions of noisy samples in order to improve the inference of probabilistic automata. Rather than definitively removing corrupted examples before the learning process, we propose a technique, based on statistical estimates and linear regression, for correcti ..."
Abstract
 Add to MetaCart
In this paper, we aim at correcting distributions of noisy samples in order to improve the inference of probabilistic automata. Rather than definitively removing corrupted examples before the learning process, we propose a technique, based on statistical estimates and linear regression, for correcting the probabilistic prefix tree automaton (PPTA). It requires a human expertise to correct only a small sample of data, selected in order to estimate the noise level. This statistical information permits us to automatically correct the whole PPTA and then to infer better models from a generalization point of view. After a theoretical analysis of the noise impact, we present a large experimental study on several datasets.