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Machine learning algorithms

Table of contents

Accumulated local effects

Almeida–Pineda recurrent backpropagation

Augmented Analytics

Backpropagation

Bioz

CN2 algorithm

Constructing skill trees

Deep reinforcement learning

Dehaene–Changeux model

Diffusion map

Diffusion model

Dominance-based rough set approach

Dynamic time warping

Error-driven learning

Evolutionary multimodal optimization

Expectation–maximization algorithm

Federated Learning of Cohorts

GeneRec

Genetic Algorithm for Rule Set Production

Graphical time warping

Growing self-organizing map

Hyper basis function network

IDistance

Incremental learning

K-nearest neighbors algorithm

Kernel methods for vector output

Kernel principal component analysis

Label propagation algorithm

Leabra

Linde–Buzo–Gray algorithm

Local outlier factor

Logic learning machine

LogitBoost

Loss functions for classification

Manifold alignment

Minimum redundancy feature selection

Mixture of experts

Multi expression programming

Multiple kernel learning

NSynth

Neural radiance field

Online machine learning

Open Syllabus Project

PVLV

Prefrontal cortex basal ganglia working memory

Prototype methods

Proximal Policy Optimization

Q-learning

Quadratic unconstrained binary optimization

Query-level feature

Quickprop

Randomized weighted majority algorithm

Repeated incremental pruning to produce error reduction (RIPPER)

Rprop

Rule-based machine learning

Self-play

Skill chaining

Sparse PCA

State–action–reward–state–action

Stochastic variance reduction

Structured kNN

T-distributed stochastic neighbor embedding

Triplet loss

Wake-sleep algorithm

Weighted majority algorithm (machine learning)

Zero-shot learning

 Ancestors (5)

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