Types of Machine Learning Algorithms

By Taiwo Oladipupo Ayodele. 2010

509 citations recorded by the source corpus.

• Supervised learning --where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --which models a set of inputs: labeled examples are not available. • Semi-supervised learning --which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --where the algorithm learns its own inductive bias based on previous experience.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/c4ae802491724aee021f31f02327b9671cead3dc

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In Artificial Intelligence

Citations in the corpus

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “An SVM algorithm, which includes optimizing a quadratic function with linear constraints (that is, the problem of constructing a set of hyperplanes), provides an alternative method to the multi-layer neural network with nonconvex and unconstrained optimization problem [39].”
Supervised Machine Learning Algorithms: Classification and Comparison (2017, methodology): “A linear classifier is often used in situations where the speed of classification is an issue, since it is rated the fastest classifier [21].”
Advancing Biosensors with Machine Learning. (2020, background): “45,46 Supervised learning refers to the ML algorithms which can be trained with a group of input data with their target outputs.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “As a consequence, instead of deep learning architectures, many researchers opted to employ SVM for classifying data points using labeled training samples [138], [142].”