The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Toshiharu Hatanaka - One of the best experts on this subject based on the ideXlab platform.

  • KES (1) - Nonlinear state estimation by evolution strategies based gaussian sum particle filter
    Lecture Notes in Computer Science, 2005
    Co-Authors: Katsuji Uosaki, Toshiharu Hatanaka
    Abstract:

    There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability Distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a novel particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the Posteriori Distribution and Evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.

  • Congress on Evolutionary Computation - Evolution strategies based Gaussian sum particle filter for nonlinear state estimation
    2005 IEEE Congress on Evolutionary Computation, 1
    Co-Authors: Katsuji Uosaki, Toshiharu Hatanaka
    Abstract:

    There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate the grid sum approximation of a posterior probability Distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the Posteriori Distribution and evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.

Takeshi Kawabata - One of the best experts on this subject based on the ideXlab platform.

  • back off method for n gram smoothing based on binomial Posteriori Distribution
    International Conference on Acoustics Speech and Signal Processing, 1996
    Co-Authors: Takeshi Kawabata, Masafumi Tamoto
    Abstract:

    The n-gram language model is powerful for treating natural spoken language, however it requires large amounts of spoken language corpus to estimate reliable model parameters. To estimate n-gram probabilities from sparse data, Katz's (1987) back-off smoothing method is promising. However, this approach is sometimes unstable because it uses singleton heuristics based on Turing's formula. This paper proposes a new back-off method based on binomial Posteriori Distribution of n-gram probabilities, which achieves stable and more effective n-gram smoothing using a sophisticated calculation formula with no heuristics.

  • clustering word category based on binomial Posteriori co occurrence Distribution
    International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: Masafumi Tamoto, Takeshi Kawabata
    Abstract:

    This paper describes a word clustering technique for stochastic language modeling and reports experimental evidence for its validity. The binomial Posteriori Distribution (BPD) distance measurement between words is introduced. It is based on word co-occurrency and reliability. We plan to consider a practical application of this clustering technology by utilizing each cluster as a Markov state in the construction of a word prediction model.

  • ICASSP - Back-off method for n-gram smoothing based on binomial Posteriori Distribution
    1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1
    Co-Authors: Takeshi Kawabata, Masafumi Tamoto
    Abstract:

    The n-gram language model is powerful for treating natural spoken language, however it requires large amounts of spoken language corpus to estimate reliable model parameters. To estimate n-gram probabilities from sparse data, Katz's (1987) back-off smoothing method is promising. However, this approach is sometimes unstable because it uses singleton heuristics based on Turing's formula. This paper proposes a new back-off method based on binomial Posteriori Distribution of n-gram probabilities, which achieves stable and more effective n-gram smoothing using a sophisticated calculation formula with no heuristics.

  • ICASSP - Clustering word category based on binomial Posteriori co-occurrence Distribution
    1995 International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Masafumi Tamoto, Takeshi Kawabata
    Abstract:

    This paper describes a word clustering technique for stochastic language modeling and reports experimental evidence for its validity. The binomial Posteriori Distribution (BPD) distance measurement between words is introduced. It is based on word co-occurrency and reliability. We plan to consider a practical application of this clustering technology by utilizing each cluster as a Markov state in the construction of a word prediction model.

Masafumi Tamoto - One of the best experts on this subject based on the ideXlab platform.

  • back off method for n gram smoothing based on binomial Posteriori Distribution
    International Conference on Acoustics Speech and Signal Processing, 1996
    Co-Authors: Takeshi Kawabata, Masafumi Tamoto
    Abstract:

    The n-gram language model is powerful for treating natural spoken language, however it requires large amounts of spoken language corpus to estimate reliable model parameters. To estimate n-gram probabilities from sparse data, Katz's (1987) back-off smoothing method is promising. However, this approach is sometimes unstable because it uses singleton heuristics based on Turing's formula. This paper proposes a new back-off method based on binomial Posteriori Distribution of n-gram probabilities, which achieves stable and more effective n-gram smoothing using a sophisticated calculation formula with no heuristics.

  • clustering word category based on binomial Posteriori co occurrence Distribution
    International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: Masafumi Tamoto, Takeshi Kawabata
    Abstract:

    This paper describes a word clustering technique for stochastic language modeling and reports experimental evidence for its validity. The binomial Posteriori Distribution (BPD) distance measurement between words is introduced. It is based on word co-occurrency and reliability. We plan to consider a practical application of this clustering technology by utilizing each cluster as a Markov state in the construction of a word prediction model.

  • ICASSP - Back-off method for n-gram smoothing based on binomial Posteriori Distribution
    1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1
    Co-Authors: Takeshi Kawabata, Masafumi Tamoto
    Abstract:

    The n-gram language model is powerful for treating natural spoken language, however it requires large amounts of spoken language corpus to estimate reliable model parameters. To estimate n-gram probabilities from sparse data, Katz's (1987) back-off smoothing method is promising. However, this approach is sometimes unstable because it uses singleton heuristics based on Turing's formula. This paper proposes a new back-off method based on binomial Posteriori Distribution of n-gram probabilities, which achieves stable and more effective n-gram smoothing using a sophisticated calculation formula with no heuristics.

  • ICASSP - Clustering word category based on binomial Posteriori co-occurrence Distribution
    1995 International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Masafumi Tamoto, Takeshi Kawabata
    Abstract:

    This paper describes a word clustering technique for stochastic language modeling and reports experimental evidence for its validity. The binomial Posteriori Distribution (BPD) distance measurement between words is introduced. It is based on word co-occurrency and reliability. We plan to consider a practical application of this clustering technology by utilizing each cluster as a Markov state in the construction of a word prediction model.

Katsuji Uosaki - One of the best experts on this subject based on the ideXlab platform.

  • KES (1) - Nonlinear state estimation by evolution strategies based gaussian sum particle filter
    Lecture Notes in Computer Science, 2005
    Co-Authors: Katsuji Uosaki, Toshiharu Hatanaka
    Abstract:

    There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability Distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a novel particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the Posteriori Distribution and Evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.

  • Congress on Evolutionary Computation - Evolution strategies based Gaussian sum particle filter for nonlinear state estimation
    2005 IEEE Congress on Evolutionary Computation, 1
    Co-Authors: Katsuji Uosaki, Toshiharu Hatanaka
    Abstract:

    There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate the grid sum approximation of a posterior probability Distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the Posteriori Distribution and evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.

Yu Hen Hu - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP (4) - Distributed particle filters for wireless sensor network target tracking
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005
    Co-Authors: Xiaohong Sheng, Yu Hen Hu
    Abstract:

    We propose two distributed particle filters to estimate and track the moving targets in a wireless sensor network. The observations by the sensors are divided into a set of disjoint uncorrelated cliques. The first distributed algorithm runs the local particle filters sequentially at each clique. The second distributed algorithm runs the local particle filters in parallel to obtain the local sufficient statistics, and then send these statistics to a centralized location through multi-hops to obtain the final estimates. The two distributed algorithms are both almost surely convergent. In addition, we proposed to use the local Gaussian mixture model (GMM) to approximate the Posteriori Distribution obtained from the local particle filter. By propagating the GMM parameters rather than belief, we achieve significant bandwidth and power consumption reduction. Very promising simulation results are reported as well.