Meta learning (computer science)
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Meta learning is a subfield of machine learning where automatic learning algorithms are applied on metadata about machine learning experiments. As of 2017 the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn.
Flexibility is 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 learning problem. A learning algorithm may perform very well in one domain, but not on the next. 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 metadata, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to learn, 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, a possibly related problem. A good analogy to meta-learning, and the inspiration for Bengio et al.'s early work (1991), considers that genetic evolution learns the learning procedure encoded in genes and executed in each individual's brain.
A proposed definition for a meta learning system combines three requirements:
- The system must include a learning subsystem.
- Experience is gained by exploiting meta knowledge extracted
- in a previous learning episode on a single dataset, or
- from different domains.
- Learning bias must be chosen dynamically.
Bias refers to the assumptions that influence the choice of explanatory hypotheses and not the notion of bias represented in the bias-variance dilemma. Meta learning is concerned with two aspects of learning bias.
- Declarative bias specifies the representation of the space of hypotheses, and affects the size of the search space (e.g., represent hypotheses using linear functions only).
- Procedural bias imposes constraints on the ordering of the inductive hypotheses (i.e. preferring smaller hypotheses).
Some approaches which have been viewed as instances of meta learning:
- Discovering meta-knowledge works by inducing knowledge (e.g. rules) that expresses how each learning method will perform on different learning problems. The metadata is formed by characteristics of the data (general, statistical, information-theoretic,... ) in the learning problem, and characteristics of the learning algorithm (type, parameter settings, performance measures,...). Another learning algorithm then learns how the data characteristics relate to the algorithm characteristics. Given a new learning problem, the data characteristics are measured, and the performance of different learning algorithms are predicted. Hence, one can predict the algorithms best suited for the new problem.
- Stacked generalisation works by combining multiple (different) learning algorithms. The metadata is formed by the predictions of those different algorithms. Another learning algorithm learns from this metadata to predict which combinations of algorithms give generally good results. Given a new learning problem, the predictions of the selected set of algorithms are combined (e.g. by (weighted) voting) to provide the final prediction. Since each algorithm is deemed to work on a subset of problems, a combination is hoped to be more flexible and able to make good predictions.
- Boosting is related to stacked generalisation, but uses the same algorithm multiple times, where the examples in the training data get different weights over each run. This yields different predictions, each focused on rightly predicting a subset of the data, and combining those predictions leads to better (but more expensive) results.
- Dynamic bias selection works by altering the inductive bias of a learning algorithm to match the given problem. This is done by altering key aspects of the learning algorithm, such as the hypothesis representation, heuristic formulae, or parameters. Many different approaches exist.
- Inductive transfer studies how the learning process can be improved over time. Metadata consists of knowledge about previous learning episodes and is used to efficiently develop an effective hypothesis for a new task. A related approach is called learning to learn, in which the goal is to use acquired knowledge from one domain to help learning in other domains.
- Other approaches using metadata to improve automatic learning are learning classifier systems, case-based reasoning and constraint satisfaction.
- Some initial, theoretical work has been initiated to use Applied Behavioral Analysis as a foundation for agent-mediated meta-learning about the performances of human learners, and adjust the instructional course of an artificial agent.
- AutoML such as Google Brain's "AI building AI" project, which according to Google briefly exceeded existing ImageNet benchmarks in 2017.
- Bengio, Yoshua; Bengio, Samy; Cloutier, Jocelyn (1991). Learning to learn a synaptic rule (PDF). IJCNN'91.
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- "Robots Are Now 'Creating New Robots,' Tech Reporter Says". NPR.org. 2018. Retrieved 29 March 2018.
- "AutoML for large scale image classification and object detection". Google Research Blog. November 2017. Retrieved 29 March 2018.
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