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  2. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/.../Automatic_Clustering_Algorithms

    Automated selection of k in a K-means clustering algorithm, one of the most used centroid-based clustering algorithms, is still a major problem in machine learning. The most accepted solution to this problem is the elbow method.

  3. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.

  4. Self-modifying code - Wikipedia

    en.wikipedia.org/wiki/Self-modifying_code

    Traditional machine learning systems have a fixed, pre-programmed learning algorithm to adjust their parameters. However, since the 1980s Jürgen Schmidhuber has published several self-modifying systems with the ability to change their own learning

  5. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). [139] It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment.

  6. Particle swarm optimization - Wikipedia

    en.wikipedia.org/wiki/Particle_swarm_optimization

    This school of thought merely tries to find PSO algorithms and parameters that cause good performance regardless of how the swarm behaviour can be interpreted in relation to e.g. exploration and exploitation. Such studies have led to the simplification of the PSO algorithm, see below.

  7. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...

  8. Self-tuning - Wikipedia

    en.wikipedia.org/wiki/Self-tuning

    Self-tuning metaheuristics have emerged as a significant advancement in the field of optimization algorithms in recent years, since fine tuning can be a very long and difficult process. [3] These algorithms differentiate themselves by their ability to autonomously adjust their parameters in response to the problem at hand, enhancing efficiency ...

  9. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. This is partly motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain. [6] An illustration of the training of a self-organizing map.