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Showing 1 to 8 of 8 for “"Baum-Welch Algorithm"”.

  1. Experiments with hidden Markov models

    … chains with generative transitions and the Baum-Welch algorithm. We explore generating the hypothesis model in various ways. We use the hypothesis model as the original model. And we investigate the feasibility of implementing a sequential version of the Baum-Welch algorithm. This research …

    reykjavik Repository record for Experiments with hidden Markov models (opens in a new tab)

  2. Two new approaches for learning Hidden Markov Models

    … to infer these hidden variables has been the Baum-Welch algorithm. This thesis utilizes insights from two related fields. The first insight is from Angluin's seminal paper on learning regular sets from queries and counterexamples, which produces a simple and intuitive algorithm that …

    mit Repository record for Two new approaches for learning Hidden Markov Models (opens in a new tab)

  3. Parameter estimation in HMMs with guaranteed convergence

    The EM (Expectation-Maximization) algorithm is a heuristic for parameter estimation in statistical models with latent variables, where explicit computation of the maximum likelihood estimate (MLE) is infeasible. Although widely used in practice, the theoretical guarantees associated with EM are …

    mit Repository record for Parameter estimation in HMMs with guaranteed convergence (opens in a new tab)

  4. One-vector representations of stochastic signals for pattern recognition

    … space, as the majority of pattern recognition algorithms by design handle stochastic signals having a one-vector representation. More importantly, a one-vector representation naturally allows for optimal distance metric learning from the data, which generally accounts for significant …

    uiuc Repository record for One-vector representations of stochastic signals for pattern recognition (opens in a new tab)

  5. An integrated performance model learning and planning approach for optimal infrastructure facility maintenance under partial observability

    … estimate of performance model using the Baum-Welch algorithm. Both offline and online versions of the learning algorithm are presented. The probing-optimizing dichotomy, also known as exploration-exploitation dilemma, in choosing between the best strategy based on the past knowledge of …

    tdl Repository record for An integrated performance model learning and planning approach for optimal infrastructure facility maintenance under partial observability (opens in a new tab)

  6. Automatic Phoneme Recognition with Segmental Hidden Markov Models

    … (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for the phoneme states. We describe in detail the three phases of the …

    vt Repository record for Automatic Phoneme Recognition with Segmental Hidden Markov Models (opens in a new tab)

  7. Android Application Install-time Permission Validation and Run-time Malicious Pattern Detection

    … size by adding game applications, to optimize Baum-Welch algorithm parameters, and to balance the size of the Intent sequence. To better emulate the participant's usage, some popular applications can be selected in advance, and the remainder can be randomly chosen.

    vt Repository record for Android Application Install-time Permission Validation and Run-time Malicious Pattern Detection (opens in a new tab)

  8. Iterative Decoding and Channel Estimation over Hidden Markov Fading Channels

    … maximum likelihood sequence estimation (MLSE) algorithms or maximum <I> a posteriori</I> (MAP) algorithms operating over the trellis defined by the MFC can be used for channel estimation. Furthermore, the thesis illustrates sequential and decision-directed techniques for using the …

    vt Repository record for Iterative Decoding and Channel Estimation over Hidden Markov Fading Channels (opens in a new tab)