Machine Learning (Theory)

6/22/2005

Languages of Learning

Tags: Organization jl@ 1:08 pm

A language is a set of primitives which can be combined to succesfully create complex objects. Languages arise in all sorts of situations: mechanical construction, martial arts, communication, etc… Languages appear to be the key to succesfully creating complex objects—it is difficult to come up with any convincing example of a complex object which is not built using some language. Since languages are so crucial to success, it is interesting to organize various machine learning research programs by language.

The most common language in machine learning are languages for representing the solution to machine learning. This includes:

  1. Bayes Nets and Graphical Models A language for representing probability distributions. The key concept supporting modularity is conditional independence. Michael Kearns has been working on extending this to game theory.
  2. Kernelized Linear Classifiers A language for representing linear separators, possibly in a large space. The key form of modularity here is kernelization.
  3. Neural Networks A language for representing and learning functions. The key concept supporting modularity is backpropagation. (Yann LeCun gave some very impressive demos at the Chicago MLSS.)
  4. Decision Trees Another language for representing and learning functions. The key concept supporting modularity is partitioning the input space.

Many other learning algorithms can be seen as falling into one of the above families.

In addition there are languages related to various aspects of learning.

  1. Reductions A language for translating between varying real-world losses and core learning algorithm optimizations.
  2. Feature Languages Exactly how features are specified varies from on learning algorithm to another. Several people have been working on languages for features that cope with sparsity or the cross-product nature of databases.
  3. Data interaction languages The statistical query model of learning algorithms provides a standardized interface between data and learning algorithm.

These lists surely miss some languages—feel free to point them out below.

With respect to research “interesting” language-related questions include:

  1. For what aspects of learning is a language missing? Anytime adhocery is encountered, this suggests that there is room for a language. Finding what is not there is both hard and valuable.
  2. Are any of these languages fundamentally flawed or fundamentally advantageous with respect to another language?
  3. What are the most easy to use and effective primitives for these languages?

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