Model-Based Machine Learning (MBML) (JNNC Technologies)

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The model-based Machine Learning methodology was introduced by a Microsoft researcher, Chris Bishop. The main goal of model-based machine learning is described as “a single framework which supports a wide range of models”. It is focused on a powerful framework based on Bayesian inference in probabilistic graphical models.

The key goals of a model-based approach include the following:

• The ability to create a very broad range of models, along with suitable inference or learning algorithms, in which many traditional machine learning techniques appear as special cases.
• Each specific model can be tuned to the individual requirements of the particular application: for example, if the application requires a combination of clustering and classification in the context of time-series data, it is not necessary to mash together traditional algorithms for each of these elements (Gaussian mixtures, neural networks and hidden Markov models (HMMs), for instance), but instead a single, integrated model capturing the desired behaviour can be constructed.
• Segregation between the model and the inference algorithm: if changes are made to the model, the corresponding modified inference software is created automatically
• Transparency of functionality: the model is described by compact code within a generic modelling language, and so the structure of the model is readily apparent.
• Pedagogy: Newcomers to the field of machine learning have only to learn a single modelling environment in order to be able to access a wide range of modelling solutions

Benefits of MBML

To overcome these challenges of traditional machine learning, the new-paradigm, MBML is introduced in order to make the process more transparent. The advantages of this methodology are mentioned below

• This methodology has an auto-generated inference algorithm
• It has an easy extension to more complex situations, a model can be modified in a flexible manner using the same inference algorithms
• The coding is compact, it is easy to write and maintain with a transparent functionality.
• Unlike traditional machine learning, it has only one simple framework.