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v. t. 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]
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 ...
e. Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, [ 1 ] including genomics, proteomics, microarrays, systems biology, evolution, and text mining. [ 2 ][ 3 ] Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein ...
Deep learning is a subset of machine learning methods that utilize neural networks for representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [1][2][3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively ...
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. AutoML was proposed as an artificial ...
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 ...
P ( X , Y ) {\displaystyle P (X,Y)} on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances (outcomes) of an observation x. [2] A discriminative model is a model of the conditional probability. P ( Y ∣ X = x ) {\displaystyle P (Y\mid X=x)} of the target Y, given an observation x.