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  2. Learning augmented algorithm - Wikipedia

    en.wikipedia.org/wiki/Learning_augmented_algorithm

    A learning augmented algorithm is an algorithm that can make use of a prediction to improve its performance. [1] Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. This extra parameter often is a prediction of some property of the solution.

  3. Neuroevolution of augmenting topologies - Wikipedia

    en.wikipedia.org/wiki/Neuroevolution_of...

    The competing conventions problem arises when there is more than one way of representing information in a phenotype. For example, if a genome contains neurons A, B and C and is represented by [A B C], if this genome is crossed with an identical genome (in terms of functionality) but ordered [C B A] crossover will yield children that are missing information ([A B A] or [C B C]), in fact 1/3 of ...

  4. Proximal policy optimization - Wikipedia

    en.wikipedia.org/wiki/Proximal_Policy_Optimization

    Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default RL algorithm at the US artificial intelligence company OpenAI. [2]

  5. Multiplicative weight update method - Wikipedia

    en.wikipedia.org/wiki/Multiplicative_Weight...

    In this case, player allocates higher weight to the actions that had a better outcome and choose his strategy relying on these weights. In machine learning, Littlestone applied the earliest form of the multiplicative weights update rule in his famous winnow algorithm, which is similar to Minsky and Papert's earlier perceptron learning algorithm ...

  6. Automated machine learning - Wikipedia

    en.wikipedia.org/wiki/Automated_machine_learning

    Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.

  7. Meta-learning (computer science) - Wikipedia

    en.wikipedia.org/wiki/Meta-learning_(computer...

    Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to 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 ...

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  9. Algorithmic learning theory - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_learning_theory

    Algorithmic learning theory investigates the learning power of Turing machines. Other frameworks consider a much more restricted class of learning algorithms than Turing machines, for example, learners that compute hypotheses more quickly, for instance in polynomial time.