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Method for discovering interesting relations between variables in databases
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended
Association_rule_learning
AI that learns decision rules from data
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves
Rule-based_machine_learning
Overview of and topical guide to machine learning
Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Association rule learning algorithms
Outline_of_machine_learning
Subset of artificial intelligence
order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems
Machine_learning
Term in data mining and association rule learning
In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying
Lift_(data_mining)
Paradigm of rule-based machine learning methods
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic
Learning_classifier_system
Process of analyzing large data sets
Anomaly/outlier/change detection Association rule learning Bayesian networks Classification Cluster analysis Decision trees Ensemble learning Factor analysis Genetic
Data_mining
Data-mining algorithm
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent
Apriori_algorithm
Model-free reinforcement learning algorithm
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
Q-learning
Market research and business management technique
reduce the search space for the problem. The support metric in the association rule learning algorithm is defined as the frequency of the antecedent or consequent
Affinity_analysis
Machine learning software
regression, factor analysis, clustering, classification and association rule learning. Tanagra is an academic project. It is widely used in French-speaking
Tanagra_(machine_learning)
Form of association rule learning
Contrast set learning is a form of association rule learning that seeks to identify meaningful differences between separate groups by reverse-engineering
Contrast_set_learning
Process of acquiring new knowledge
of learning language and communication, and the stage where a child begins to understand rules and symbols. This has led to a view that learning in organisms
Learning
Data mining technique
processing algorithms and itemset mining which is typically based on association rule learning. Local process models extend sequential pattern mining to more
Sequential_pattern_mining
Area of machine learning
tools are machine learning libraries for Python, like scikit-learn. Some major rule induction paradigms are: Association rule learning algorithms (e.g.
Rule_induction
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Statistics and machine learning technique
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from
Ensemble_learning
Machine learning technique
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related
Transfer_learning
Optimization algorithm for artificial neural networks
In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is
Backpropagation
Type of feedforward neural network
In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation
Multilayer_perceptron
Machine learning strategy
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)
Active learning (machine learning)
Active_learning_(machine_learning)
AI's tendency to abruptly and drastically forget old info after learning new info
learning rule for training neural networks, called the 'novelty rule', to help alleviate catastrophic interference. As its name suggests, this rule helps
Catastrophic_interference
Method in natural language processing
meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors
Word_embedding
Process of automating the application of machine learning
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination
Automated_machine_learning
Paradigm in machine learning that uses no classification labels
backpropagation, unsupervised learning also employs other methods including: Hopfield learning rule, Boltzmann learning rule, Contrastive Divergence, Wake
Unsupervised_learning
Topics referred to by the same term
Eclat Textile, a Taiwanese textile company Lotus Eclat, a car Association rule learning § Eclat algorithm, an algorithm This disambiguation page lists
Eclat
2018 text-generating language model
primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets
GPT-1
Machine-learning and computational-neuroscience conference
Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Type of artificial neural network
The Journal of Machine Learning Research. 3: 1137–1155. Auer, Peter; Harald Burgsteiner; Wolfgang Maass (2008). "A learning rule for very simple universal
Feedforward_neural_network
Set of learning techniques in machine learning
In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations
Feature_learning
Machine learning methods using multiple input modalities
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images
Multimodal_learning
Problem setup in machine learning
Zero-shot learning (ZSL) is a problem setup in machine learning where, at test time, a learner observes samples from classes which were not observed during
Zero-shot_learning
Research field that lies at the intersection of machine learning and computer security
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques
Adversarial_machine_learning
Algorithm for supervised learning of binary classifiers
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether
Perceptron
Technique for database mining
recommendation systems For the most part, FP discovery can be done using association rule learning with particular algorithms Eclat, FP-growth and the Apriori algorithm
Frequent_pattern_discovery
Type of large language model
generative artificial intelligence chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large datasets
Generative pre-trained transformer
Generative_pre-trained_transformer
Paradigm in machine learning
p(x|y)p(y)} by Bayes' rule. Semi-supervised learning with generative models can be viewed either as an extension of supervised learning (classification plus
Weak_supervision
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Academic conference in machine learning
International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the oldest
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Machine learning technique
from are based on a consistent and simple rule. Both offline data collection models, where the model is learning by interacting with a static dataset and
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Tasks in machine learning
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals
Self-supervised_learning
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
Type of activation function
silencing of the parts of the model found to be stimuli-irrelevant during learning that allows for scaling. As the stimuli-irrelevant proportion of the model
Rectified_linear_unit
Computer programming concept
the value function for the current state using the rule: V ( S t ) ← ( 1 − α ) V ( S t ) + α ⏟ learning rate [ R t + 1 + γ V ( S t + 1 ) ⏞ The TD target
Temporal_difference_learning
Smooth approximation of one-hot arg max
term "softargmax", though the term "softmax" is conventional in machine learning. This section uses the term "softargmax" for clarity. Formally, instead
Softmax_function
Class of artificial neural network
middle layer contains recurrent connections that change by a Hebbian learning rule. Later, in Principles of Neurodynamics (1961), he described "closed-loop
Recurrent_neural_network
Taiwanese American Professor
Li, Haosong; Sheu, Phillip C.-Y. (2022-03-28). "A scalable association rule learning and recommendation algorithm for large-scale microarray datasets"
Phillip_C.-Y._Sheu
Optimization algorithm
become an important optimization method in machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective
Stochastic_gradient_descent
Integrated circuit technology
digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing
Neuromorphic_computing
Machine learning technique
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous
Mixture_of_experts
Models used to produce word embeddings
Rong, Xin (5 June 2016), word2vec Parameter Learning Explained, arXiv:1411.2738 Hinton, Geoffrey E. "Learning distributed representations of concepts."
Word2vec
Subdiscipline of artificial intelligence
network Relational Markov network Relational Kalman filtering Association rule learning Formal concept analysis Fuzzy logic Grammar induction Knowledge
Statistical relational learning
Statistical_relational_learning
Machine learning technique
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization
Normalization (machine learning)
Normalization_(machine_learning)
Density-based data clustering algorithm
distance), and minPts is then the desired minimum cluster size. MinPts: As a rule of thumb, a minimum minPts can be derived from the number of dimensions D
DBSCAN
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Automated recognition of patterns and regularities in data
retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some
Pattern_recognition
Difficulties arising when analyzing data with many aspects ("dimensions")
A typical rule of thumb is that there should be at least 5 training examples for each dimension in the representation. In machine learning and insofar
Curse_of_dimensionality
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Deep learning library
PyTorch is an open-source deep learning library, originally developed by Meta Platforms and currently developed with support from the Linux Foundation
PyTorch
Flaw in mathematical modelling
overfitting occurs when a model begins to "memorize" training data rather than "learning" to generalize from a trend. As an extreme example, if the number of parameters
Overfitting
Reverse-engineering neural networks
identify structures, circuits or algorithms encoded in the weights of machine learning models. This contrasts with earlier interpretability methods that focused
Mechanistic_interpretability
Memory unit used in neural networks
Bahdanau, Dzmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (2014). "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine
Gated_recurrent_unit
Software user interface
context of machine learning.It is also used in conversational AI to manage complex interactions that require human empathy. In machine learning, HITL is used
Human-in-the-loop
Machine learning paradigm using minimal training data
Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small
Few-shot_learning
given its inputs ALOPEX: a correlation-based machine-learning algorithm Association rule learning: discover interesting relations between variables, used
List_of_algorithms
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem
Deep_reinforcement_learning
Book on data science applications
Regression Models Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines Apriori Association Rules Learning k-Means Clustering Model Performance
Data Science and Predictive Analytics
Data_Science_and_Predictive_Analytics
Conversational software
partner. Chatbots have existed for decades, but chatbots based on deep learning have gained popularity during the AI boom of the 2020s, with the releases
Chatbot
Machine learning calibration technique
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution
Platt_scaling
Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Similarity measure for number sequences
techniques. This normalised form distance is often used within many deep learning algorithms. In biology, there is a similar concept known as the Otsuka–Ochiai
Cosine_similarity
Model-free reinforcement learning algorithm
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient
Proximal_policy_optimization
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is
Transformer_(deep_learning)
Deep learning method
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence
Generative adversarial network
Generative_adversarial_network
Statistical model of language
approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discrete representations like word n-gram language
Language_model
perceptron learning algorithm. The aforementioned least mean squares (LMS) algorithm, also known as the Widrow–Hoff learning rule or the Delta rule, was more
History of artificial neural networks
History_of_artificial_neural_networks
Type of artificial intelligence system
features to encode images, and n-gram or rule-based text templates to generate descriptions. With the rise of deep learning, neural networks became dominant in
Vision-language_model
Property of a model
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions
Bias–variance_tradeoff
Learning model
Double-loop learning entails the modification of goals or decision-making rules in the light of experience. In double-loop learning, individuals or organizations
Double-loop_learning
Method of machine learning
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update
Online_machine_learning
Theory of machine learning
tolerance (PAC learning) Grammar induction Information theory Occam learning Stability (learning theory) "ACL - Association for Computational Learning". Valiant
Computational_learning_theory
Machine learning model training problem
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered
Vanishing_gradient_problem
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Machine learning model for vision processing
exaFLOPs. Transformer (machine learning model) Convolutional neural network Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow All positional
Vision_transformer
Machine-learning process
inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as
Grammar_induction
Class of artificial neural networks
passing" for such approaches. In the more general subject of "geometric deep learning", certain existing neural network architectures can be interpreted as GNNs
Graph_neural_network
Tree-based ensemble machine learning methods
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude
Random_forest
Artificial neural network that mimics neurons
resolving it include: resorting to entirely biologically inspired local learning rules for the hidden units translating conventionally trained "rate-based"
Spiking_neural_network
Iterative method for finding maximum likelihood estimates in statistical models
local optima. Hence, a need exists for alternative methods for guaranteed learning, especially in the high-dimensional setting. Alternatives to EM exist with
Expectation–maximization algorithm
Expectation–maximization_algorithm
Computational model used in machine learning
the weights of an Ising model by Hebbian learning rule as a model of associative memory, adding in learning. This was popularized as the Hopfield network
Neural network (machine learning)
Neural_network_(machine_learning)
Machine learning technique
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence
Attention_(machine_learning)
Object categorization problem
algorithm Bayesian inference Feature detection Association rule learning Hopfield network Zero-shot learning Li, Fergus & Perona 2002. sfn error: no target:
One-shot learning (computer vision)
One-shot_learning_(computer_vision)
Deep learning generative model to encode data representation
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013
Variational_autoencoder
Technique for setting initial values of trainable parameters in a neural network
In deep learning, weight initialization or parameter initialization describes the initial step in creating a neural network. A neural network contains
Weight_initialization
Use of technology in education to enhance learning and teaching
several domains, including learning theory, computer-based training, online learning, and mobile learning (m-learning). The Association for Educational Communications
Educational_technology
Deep learning architecture
Mamba is a deep learning architecture focused on sequence modeling. It was developed by two researchers Albert Gu from Carnegie Mellon University and Tri
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
2019 text-generating language model
exaggerated; Anima Anandkumar, a professor at Caltech and director of machine learning research at Nvidia, said that there was no evidence that GPT-2 had the
GPT-2
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING
ASSOCIATION RULE-LEARNING