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e. Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [ 1] Recently, artificial neural networks have been able to surpass many previous approaches in ...
e. 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. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
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. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.
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 model ). Learning is the process of apprehending useful knowledge by observing and interacting with the world. [2]
The intermediate (thin) rectangles represent the hypotheses in the version space. Version space learningis a logicalapproach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction[1]
Online machine learning, from the work of Nick Littlestone [citation needed]. While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms. For example, PAC theory inspired boosting, VC theory led to support vector machines, and Bayesian inference led to belief ...
One model of a machine learning is producing a function, f(x), which given some information, x, predicts some variable, y, from training data and . It is distinct from mathematical optimization because f {\displaystyle f} should predict well for x {\displaystyle x} outside of X train {\displaystyle X_{\text{train}}} .
Similarity learning. Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn a similarity function that measures how similar or related two objects are. It has applications in ranking, in recommendation systems, visual identity ...