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Showing 1 to 6 of 6 for “"Sequential data analysis"”.

  1. Deep learning in sequential data analysis

    … to look into deep learning based methods for sequential data such as videos and medical image sequences. With the extra information from its additional sequential dimension, sequential data naturally raises an important and challenging question: How can we effectively and efficiently integrate …

    uiuc Repository record for Deep learning in sequential data analysis (opens in a new tab)

  2. Human Resource Management and Black Teachers’ Employee Experience in K-12 Urban Public Schools

    <p>National teacher data indicate that publicly funded schools in the United States struggle to retain Black teachers in high-need urban areas and in comparison to other groups (Carver-Thomas & Darling Hammond, 2017). This qualitative investigation employed case study methodology. The case study …

    usd-thes Repository record for Human Resource Management and Black Teachers’ Employee Experience in K-12 Urban Public Schools (opens in a new tab)

  3. Information-centric Algorithms for Feature Extraction in High-Dimensional Sequential Data

    Hidden Markov Models (HMMs) are a cornerstone of sequential data analysis, offering a robust framework for modeling observable events influenced by hidden internal states. With applications spanning speech recognition, video analysis, bioinformatics, and financial time series, HMMs enable the …

    mit Repository record for Information-centric Algorithms for Feature Extraction in High-Dimensional Sequential Data (opens in a new tab)

  4. Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics

    … to help infer patterns, clusters or segments in data. In traditional nonparametric mixture models, observations are usually assumed exchangeable, even though dependence often exists associated with the space or time at which data are generated.</p> <p>Focused on model-based clustering and …

    duke Repository record for Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics (opens in a new tab)

  5. Participation and triangulation: learning from non-institutional international Architecture Live Projects through a comparative case approach

    … Mixed methods were utilised for concurrent data collection during and after each case study — participant observation, semi-structured interviews and post-occupancy participatory walking probe. Following this, three phases of sequential data analysis were employed to measure, contextualise, …

    cork Repository record for Participation and triangulation: learning from non-institutional international Architecture Live Projects through a comparative case approach (opens in a new tab)

  6. Sequential methodology and applications in sports rating

    Sequential methods aim to update beliefs about a set of parameters given new blocks of data that arise in sequence. Early research in this area was motivated by the case where the blocks of data arise in time and as a result of observing an underlying dynamical system, but an important modern …

    lancaster