Meta-Learning is a subfield of machine learning where automatic learning algorithms are applied on meta-data. In brief, it means Learning to Learn. The main goal is to use meta-data to understand how automatic learning can become flexible in solving different kinds of learning problems, hence to improve the performance of existing learning algorithms. Which means that how effectively we can increase the learning rate of our algorithms.
Meta-Learning affects the hypothesis space for the learning algorithm by either:
- Changing the hypothesis space of the learning algorithms (hyper-parameter tuning, feature selection)
- Changing the way the hypothesis space is searched by the learning algorithms (learning rules)
Variations of Meta-Learning:
- Algorithm Learning (selection) – Select learning algorithms according to the characteristics of the instance.
- Hyper-parameter Optimization – Select hyper-parameters for learning algorithms. The choice of the hyper-parameters influences how well you learn.
- Ensemble Methods – Learn to learn “collectively” – Bagging, Boosting, Stacked Generalization.
Flexibility is very important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. This means that it will only learn well if the bias matches the data in the learning problem. A learning algorithm may perform very well on one learning problem, but very badly on the next. From a non-expert point of view, this poses strong restrictions on the use of machine learning or data mining techniques, since the relationship between the learning problem (often some kind of database) and the effectiveness of different learning algorithms is not yet understood.
By using different kinds of meta-data, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to select, alter or combine different learning algorithms to effectively solve a given learning problem. Critiques of meta-learning approaches bear a strong resemblance to the critique of metaheuristic, which can be said to be a related problem.
Metalearning may be the most ambitious but also the most rewarding goal of machine learning. There are few limits to what a good meta-learner will learn. Where appropriate it will learn to learn by analogy, by chunking, by planning, by subgoal generation, etc.