Category

Machine learning algorithms

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support vector machine

set of methods for supervised statistical learning

k-means clustering

Vector quantization algorithm that minimizes the sum of squared deviations between points and their nearest mean

backpropagation

In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates.

statistical algorithm that is used to cluster points of data in functional groups

naive Bayes classifier

classification algorithm

k-nearest neighbors algorithm

classification algorithm

expectation–maximization algorithm

iterative method for finding maximum likelihood estimates in statistical models

self-organizing map

machine learning technique useful for dimensionality reduction

'''Q-learning''' is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring a model of the environment (model-free). It can handle problems with stochastic transitions and rewards without requiring adaptations.

decision tree learning

machine learning algorithm

online machine learning

a method where a model is trained incrementally on data as it becomes available, in contrast to batch learning where the entire dataset is used at once

stochastic gradient descent

gradient descent method used for the minimization of an objective function

diffusion model

deep learning algorithm

dynamic time warping

algorithm for measuring similarity between two temporal sequences, which may vary in speed

bootstrap aggregating

ensemble method within machine learning

recursive self-improvement

artificial intelligence that can modify itself to further improve its capability

t-Distributed Stochastic Neighbor Embedding

technique for dimensionality reduction

local outlier factor

AdaBoost (short for Adaptive Boosting) is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many types of learning algorithm to improve performance. The output of multiple weak learners is combined into a weighted sum that represents the final output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals of real values.

non-negative matrix factorization

algorithms for matrix decomposition

mixture of experts

machine learning technique

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only mentioned as a footnote.

deep reinforcement learning

techniques combining deep learning and reinforcement learning principles to create efficient machine learning algorithms

zero-shot learning

problem setup in machine learning, where at test time, a learner observes samples from classes that were not observed during training, and needs to predict the class they belong to

radial basis function network

an artificial neural network that uses radial basis functions as activation functions

statistical method

Gaussian splatting

volume rendering technique

Proximal Policy Optimization

model-free reinforcement learning algorithm

forward–backward algorithm

hidden Markov model inference algorithm which computes the posterior marginals of all hidden state variables given a sequence of observations, making use of dynamic programming to make only 2 passes: one forward, one backward

Neural radiance field

3D reconstruction technique using machine-learning

incremental learning

method of machine learning

Loss functions for classification

Concept in machine learning

rule-based machine learning

machine learning methods that try to develop rules that are to be applied in particular contexts

function for machine learning algorithms

Federated Learning of Cohorts

type of web tracking based on browsing history

Learning Vector Quantization