Browsing by Subject "Machine learning"

Now showing items 1-3 of 3

  • Factorial Hidden Markov Models 

    Unknown author (1996-02-09)
    We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum ...

  • Gaussian processes for state space models and change point detection 

    Turner, Ryan Darby (University of CambridgeDepartment of Engineering, 2012-02-07)
    This thesis details several applications of Gaussian processes (GPs) for enhanced time series modeling. We first cover different approaches for using Gaussian processes in time series problems. These are extended to the ...

  • Generalised Bayesian matrix factorisation models 

    Mohamed, Shakir (University of CambridgeDepartment of EngineeringSt John's College, 2011-03-15)
    Factor analysis and related models for probabilistic matrix factorisation are of central importance to the unsupervised analysis of data, with a colourful history more than a century long. Probabilistic models for matrix ...