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A gaussian prior for smoothing maximum entropy models
, 1999
"... In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood training for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address this problem ..."
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Cited by 234 (2 self)
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In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood training for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in maximum entropy smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing ngram language models. Because of the mature body of research in ngram model smoothing and the close connection between maximum entropy and conventional ngram models, this domain is wellsuited to gauge the performance of maximum entropy smoothing methods. Over a large number of data sets, we find that an ME smoothing method proposed to us by Lafferty [1] performs as well as or better than all other algorithms under consideration. This general and efficient method involves using a Gaussian prior on the parameters of the model and selecting maximum a posteriori instead of maximum likelihood parameter values. We contrast this method with previous ngram smoothing methods to explain its superior performance.
A survey of smoothing techniques for ME models
 IEEE Transactions on Speech and Audio Processing
, 2000
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Eigenvalue spacings for regular graphs
 IN IMA VOL. MATH. APPL
, 1999
"... We carry out a numerical study of fluctuations in the spectrum of regular graphs. Our experiments indicate that the level spacing distribution of a generic kregular graph approaches that of the Gaussian Orthogonal Ensemble of random matrix theory as we increase the number of vertices. A review of ..."
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Cited by 10 (5 self)
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We carry out a numerical study of fluctuations in the spectrum of regular graphs. Our experiments indicate that the level spacing distribution of a generic kregular graph approaches that of the Gaussian Orthogonal Ensemble of random matrix theory as we increase the number of vertices. A review of the basic facts on graphs and their spectra is included.
A Hybrid MaxEnt/HMM based ASR System
, 2005
"... The aim of this work is to develop a practical framework, which extends the classical Hidden Markov Models (HMM) for continuous speech recognition based on the Maximum Entropy (MaxEnt) principle. The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist ..."
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Cited by 2 (1 self)
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The aim of this work is to develop a practical framework, which extends the classical Hidden Markov Models (HMM) for continuous speech recognition based on the Maximum Entropy (MaxEnt) principle. The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist speech recognition systems. In particular, a new acoustic modelling based on discriminative MaxEnt models is formulated and is being developed to replace the generative Gaussian Mixture Models (GMM) commonly used to model acoustic variability. Initial experimental results using the TIMIT phone task are reported.
Assessing Estuarine Biota in Southern California 1
"... In southern California, most estuarine wetlands are gone, and what little habitat remains is degraded. For this reason, it is often of interest to assess the condition of estuaries over time, such as when determining the success of a restoration project. To identify impacts or opportunities for rest ..."
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In southern California, most estuarine wetlands are gone, and what little habitat remains is degraded. For this reason, it is often of interest to assess the condition of estuaries over time, such as when determining the success of a restoration project. To identify impacts or opportunities for restoration, we also may want to know how a particular estuary, or area within an estuary, compares with neighboring areas. Comparisons among wetlands require knowledge of different estuary types. The seven types of estuaries described in this paper can be easily grouped into two functional types, fully tidal and seasonally tidal, based on a simple biotic index: presence of horn snails. A description of the distribution, diversity, and abundance of organisms in estuaries is one way to assess resources, determine the success of a habitat restoration, and compare estuaries to evaluate the biotic consequences of degradation. In this review, I summarize techniques that may be useful for managers charged with biotic inventory and monitoring, emphasizing techniques to categorize wetlands and quantify plants, invertebrates, fishes, birds, and trematode parasites.
Acoustic Space Dimensionality Selection And Combination Using The
 in Proc. IEEE ICASSP
, 2004
"... In this paper we propose a discriminative approach to acoustic space dimensionality selection based on maximum entropy modelling. We form a set of constraints by composing the acoustic space with the space of phone classes, and use a continuous feature formulation of maximum entropy modelling to sel ..."
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In this paper we propose a discriminative approach to acoustic space dimensionality selection based on maximum entropy modelling. We form a set of constraints by composing the acoustic space with the space of phone classes, and use a continuous feature formulation of maximum entropy modelling to select an optimal feature set. The suggested approach has two steps: (1) the selection of the best acoustic space that efficiently and economically represents the acoustic data and its variability; (2) the combination of selected acoustic features in the maximum entropy framework to estimate the posterior probabilities over the phonetic labels given the acoustic input. Specific contributions of this paper include a parameter estimation algorithm (generalized improved iterative scaling) that enables the use of negative features, the parameterization of constraint functions using Gaussian mixture models, and experimental results using the TIMIT database.