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Ensemble learning trains two or more Machine Learning algorithms to a specific classification or regression task. The algorithms within the ensemble learning model are generally referred as "base models", "base learners" or "weak learners" in literature. The base models can be constructed using a single modelling algorithm or several different ...
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs.
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant.
In machine learning, crowd wisdom is achieved through ensemble learning. For many problems, the result obtained from an ensemble, a combination of machine learning models, can be more accurate ...
In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models ...
Inductive bias The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. [1] Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g. step-functions in decision trees instead of continuous function in a linear regression ...
Vapnik–Chervonenkis dimension. In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion can be extended to classes of binary functions. It is defined as the cardinality of the largest set of points that ...
Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. [ 164 ][ 165 ][ 166 ] Running machine learning model in embedded devices removes the need for transferring and storing data on ...