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Concept on humans' and animals' use of past learning in present situations
Generalization is the concept that humans, other animals, and artificial neural networks use past learning in present situations of learning if the conditions
Generalization_(learning)
Subset of artificial intelligence
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn
Machine_learning
Phase transition in machine learning
In machine learning, grokking, or delayed generalization, is a phenomenon observed in some settings where a model abruptly transitions from overfitting
Grokking_(machine_learning)
Form of abstraction
context (e.g. generalization in psychology, generalization in learning). In general, given two related concepts A and B, A is a "generalization" of B (equiv
Generalization
Topics referred to by the same term
specific instances. Generalization may also refer to: Generalization (learning), a concept in learning theory Faulty generalization, an informal fallacy
Generalization (disambiguation)
Generalization_(disambiguation)
Measure of algorithm accuracy
For supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error
Generalization_error
Machine learning paradigm
from the training examples, a quality measured by its generalization error. Supervised learning is commonly used for tasks such as classification (predicting
Supervised_learning
Process of acquiring new knowledge
Learning is the process of acquiring new understanding, knowledge, behavior, skills, values, attitudes, and preferences. The ability to learn is possessed
Learning
Branch of machine learning
machine learning. It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization, respectively
Deep_learning
Framework for mathematical analysis of machine learning
In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions
Probably approximately correct learning
Probably_approximately_correct_learning
Field of machine learning
S2CID 211259373. Y Ren; J Duan; S Li (2020). "Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic". 2020 IEEE
Reinforcement_learning
Statistics and machine learning technique
one that works best". Gating is a generalization of Cross-Validation Selection. It involves training another learning model to decide which of the models
Ensemble_learning
Problem setup in machine learning
semi-supervised like manner (or transductive learning). Unlike standard generalization in machine learning, where classifiers are expected to correctly
Zero-shot_learning
Principle in artificial intelligence
assumptions, generalization is impossible". More recently, "The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning" continues
Bitter_lesson
Computational model used in machine learning
In machine learning, a neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological
Neural network (machine learning)
Neural_network_(machine_learning)
Technique to make a model more generalizable and transferable
multiple neural network architectures at once to improve generalization. Empirical learning of classifiers (from a finite data set) is always an underdetermined
Regularization_(mathematics)
Machine learning technique
November 2016). "Understanding deep learning requires rethinking generalization". International Conference on Learning Representations. Clark, Jack; Amodei
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Method of logical reasoning
differences in how their results are regarded. A generalization (more accurately, an inductive generalization) proceeds from premises about a sample to a conclusion
Inductive_reasoning
Algorithm for modelling sequential data
ISBN 978-0-262-29140-8. Giles, C. Lee; Maxwell, Tom (December 1987). "Learning, invariance, and generalization in high-order neural networks". Applied Optics. 26 (23):
Transformer_(deep_learning)
Decentralized machine learning
the first international challenge on federated learning. Federated averaging (FedAvg) is a generalization of FedSGD which allows nodes to do more than one
Federated_learning
Process of finding the optimal set of variables for a machine learning algorithm
of the generalization performance of the model, taking into account the bias due to the hyperparameter optimization. Automated machine learning Neural
Hyperparameter_optimization
Notion in computational learning theory
computational learning theory in the 2000s when it was shown to have a connection with generalization. It was shown that for large classes of learning algorithms
Stability_(learning_theory)
Paradigm in machine learning that uses no classification labels
the generalization of matrices to higher orders as multi-dimensional arrays. In particular, the method of moments is shown to be effective in learning the
Unsupervised_learning
Model for music education
aural/oral learning is the most basic element of discrimination learning, generalization is the basic element of inference learning. Generalization consists
Gordon_music_learning_theory
Machine learning paradigm
Miguel (2020). "Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models". Proceedings of the 2020 Conference
Self-supervised_learning
Term in educational psychology
Keller, and Kedar-Cabelli in 1986 and called explanation-based generalization, is that learning occurs through progressive generalizing.2 This theory was first
Concept_learning
Overview of and topical guide to machine learning
boosting Random Forest Stacked Generalization Meta-learning Inductive bias Metadata Reinforcement learning Q-learning State–action–reward–state–action
Outline_of_machine_learning
Theory of learning and behaviour
likelihood that generalization from related situations would produce behaviors in new ones. Bandura went on to write the book Social Learning Theory in 1977
Social_learning_theory
Machine learning method to transfer knowledge from a large model to a smaller one
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large
Knowledge_distillation
Research field in deep learning
(2023-07-03). "Generalization Bounds using Data-Dependent Fractal Dimensions". Proceedings of the 40th International Conference on Machine Learning. PMLR: 8922–8968
Topological_deep_learning
Educational psychology concept
continuum, with no bright line between learning and transfer. Transfer may also be referred to as generalization, B. F. Skinner's concept of a response
Transfer_of_learning
Cognitive psychology process
Generalization (learning) Knowledge representation and reasoning Memory Memory Encoding Merge (linguistics) Method of loci Mnemonic Sequence learning
Chunking_(psychology)
Framework for machine learning
Niyogi, P. Poggio, T., and Rifkin, R. 2006. Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency
Statistical_learning_theory
Mathematical problem in cryptography
2005 (who won the 2018 Gödel Prize for this work); it is a generalization of the parity learning problem. Regev showed that the LWE problem is as hard to
Learning_with_errors
Structuring text as input to generative artificial intelligence
in-context learning is temporary. Training models to perform in-context learning can be viewed as a form of meta-learning, or "learning to learn". Research
Prompt_engineering
Machine learning strategy
points that would most reduce the model's generalization error. Exponentiated Gradient Exploration for Active Learning: In this paper, the author proposes a
Active learning (machine learning)
Active_learning_(machine_learning)
Interdisciplinary research area
learning theory pursues a mathematical analysis of the quantum generalizations of classical learning models and of the possible speed-ups or other improvements
Quantum_machine_learning
Ability to respond differently to different stimuli
considered to be more advanced than learning styles such as generalization and yet simultaneously acts as a basic unit to learning as a whole. The complex and
Discrimination_learning
Flaw in mathematical modelling
selection Feature engineering Freedman's paradox Generalization error Goodness of fit Grokking (machine learning) Life-time of correlation Model selection Researcher
Overfitting
Key tenet of behavioral analysis
wavelengths. This procedure yielded sharper generalization gradients than did the simple generalization procedure used in the first procedure. In addition
Stimulus_control
Type of company
In business management, a learning organization is a company that facilitates the learning of its members and continuously transforms itself. The concept
Learning_organization
Neuroscientific theory
attempt to explain synaptic plasticity, the adaptation of neurons during the learning process. Hebbian theory was introduced by Donald Hebb in his 1949 book
Hebbian_theory
Machine learning that combines deep learning and reinforcement learning
reinforcement learning studies the problems introduced in this setting. The promise of using deep learning tools in reinforcement learning is generalization: the
Deep_reinforcement_learning
Theory that describes how students receive, process, and retain knowledge during learning
of learning: Contemporary research and applications. Academic Press. McKeough, A., 2013. Teaching for transfer: Fostering generalization in learning. Routledge
Learning_theory_(education)
Machine learning technique
1109/34.730558. Giles, C. Lee; Maxwell, Tom (1987-12-01). "Learning, invariance, and generalization in high-order neural networks". Applied Optics. 26 (23):
Attention_(machine_learning)
Type of supervised learning in machine learning
Brown (2005) describe another generalization of the standard model, which they call "generalized multiple instance learning" (GMIL). The GMIL assumption
Multiple_instance_learning
Ensemble learning method
Yoav; Schapire, Robert E. (1997). "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting" (PDF). Journal of Computer
Boosting_(machine_learning)
Plot of machine learning model performance over time or experience
and generalization curve. More abstractly, learning curves plot the difference between learning effort and predictive performance, where "learning effort"
Learning curve (machine learning)
Learning_curve_(machine_learning)
Theory of cognition
The universal law of generalization is a theory of cognition stating that the probability of a response to one stimulus being generalized to another is
Universal law of generalization
Universal_law_of_generalization
Object categorization problem
deprecated parameter |citeseerx= (help) Bart; Ullman (2005). "Cross-generalization: learning novel classes from a single example by feature replacement" (PDF)
One-shot learning (computer vision)
One-shot_learning_(computer_vision)
"Budgeted Learning of Naive-Bayes Classifiers". arXiv:1212.2472 [cs.LG]. Lebowitz, Michael (1984). Concept Learning in a Rich Input Domain: Generalization-Based
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Technique in machine learning
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"
Curriculum_learning
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
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
Phenomenon in neural networks
may not be the cause, and that it may be the process of "recursive generalization" instead. The dominant explanation for polysemanticity is the superposition
Polysemanticity
Property of a model
bias–variance decomposition is a way of analyzing a learning algorithm's expected generalization error with respect to a particular problem as a sum of
Bias–variance_tradeoff
Parameter controlling the machine learning process
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters
Hyperparameter (machine learning)
Hyperparameter_(machine_learning)
Research field that lies at the intersection of machine learning and computer security
Rademacher Complexity for Adversarially Robust Generalization. International Conference on Machine Learning. Ribeiro, Antônio H.; Zachariah, Dave; Bach,
Adversarial_machine_learning
Concept of game theory
Swap regret is a concept from online learning and game theory. It is a generalization of regret in a repeated, n-decision game. In each round t, the learner
Swap_regret
Relation between sides of a right triangle
between two points, A=(a1,a2,…,an) and B=(b1,b2,…,bn), is defined, by generalization of the Pythagorean theorem, as: (a1−b1)2+(a2−b2)2+⋯+(an−bn)2=∑i=1n(ai−bi)2
Pythagorean_theorem
Subfield of machine learning
inductive biases via fast parameterization for rapid generalization. The core idea in metric-based meta-learning is similar to nearest neighbors algorithms, which
Meta-learning (computer science)
Meta-learning_(computer_science)
Measurable property or characteristic
features. These processes can facilitate learning and improve the generalization and interpretability of machine learning models. Feature selection and extraction
Feature_(machine_learning)
Solving multiple machine learning tasks at the same time
following characterization: Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained
Multi-task_learning
Type of associative learning process for behavioral modification
Operant conditioning, also called instrumental conditioning, is a learning process in which voluntary behaviors are modified by association with the addition
Operant_conditioning
Paradigm in machine learning
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the
Weak_supervision
1965 book by Robert M. Gagné
Enhancing retention and transfer (generalization) These events should satisfy or provide the necessary conditions for learning and serve as the basis for designing
Conditions_of_Learning
Speech disorder
communicative meaning"). The use of echolalia in task response to facilitate generalization is an area that holds much promise. Research in this area is certainly
Echolalia
Method in machine learning
incurring larger generalization error. Regularization, in the context of machine learning, refers to the process of modifying a learning algorithm so as
Early_stopping
Machine learning technique
"learning rate". Empirically, it has been found that using small learning rates (such as ν<0.1) yields dramatic improvements in models' generalization
Gradient_boosting
Feature of artificial neural networks
(2018-02-15). "Sensitivity and Generalization in Neural Networks: an Empirical Study". International Conference on Learning Representations. arXiv:1802.08760
Large width limits of neural networks
Large_width_limits_of_neural_networks
Type of large language model
artificial intelligence chatbots. GPTs are based on the transformer, a deep learning architecture. They are pre-trained on large datasets of unlabeled content
Generative pre-trained transformer
Generative_pre-trained_transformer
grouped under headings such as relevance, ambiguity, presumption, faulty generalization, and faulty causal reasoning, although the classification and boundaries
List_of_fallacies
Type of statistical inference
In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases
Transduction (machine learning)
Transduction_(machine_learning)
Use of machine learning to rank items
the general idea of MLR in 1992, describing learning approaches in information retrieval as a generalization of parameter estimation; a specific variant
Learning_to_rank
Biological process
discriminate between these and different-tasting live prey. Stimulus generalization is another learning phenomenon that can be illustrated by conditioned taste aversion
Conditioned_taste_aversion
Optimization algorithm
RMSProp has shown good adaptation of learning rate in different applications. RMSProp can be seen as a generalization of Rprop and is capable to work with
Stochastic_gradient_descent
Tasks in machine learning
Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning". Journal of Analysis and Testing. 2 (3). Springer
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Concept in machine learning
compression-based generalization bounds, which show that less complex models are expected to generalize better under a Solomonoff prior. Grokking (machine learning) Neural
Double_descent
Multiple hypotheses explain the possible connections between sleep and learning in humans. Research indicates that sleep does more than allow the brain
Sleep_and_learning
Foundation model allowing control of robot actions
In robot learning, a vision–language–action model (VLA) is a class of multimodal foundation models that integrates vision, language and actions. Given
Vision–language–action_model
Type of kernel induced by artificial neural networks
enables simple closed form equations describing the training dynamics, generalization, and predictions of wide neural networks. The NTK was introduced in
Neural_tangent_kernel
Aspect of learning procedure
shared elements help to account for stimulus generalization and other phenomena that may depend upon generalization. Also, different elements within the same
Classical_conditioning
Smooth approximation of one-hot arg max
numbers into a probability distribution over K possible outcomes. It is a generalization of the logistic function to multiple dimensions and is used in multinomial
Softmax_function
Projection of data onto lower-dimensional manifolds
versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction,
Nonlinear dimensionality reduction
Nonlinear_dimensionality_reduction
Machine learning technique
scales in input data, reduce overfitting, and produce better model generalization to unseen data. Normalization techniques are often theoretically justified
Normalization (machine learning)
Normalization_(machine_learning)
Machine learning method
In machine learning, ensemble averaging is the process of creating multiple models (typically artificial neural networks) and combining them to produce
Ensemble averaging (machine learning)
Ensemble_averaging_(machine_learning)
order to make generalizations or form concepts from training examples. It is also linked with Encoding (memory) to help with Learning. An example of
Explanation-based_learning
Generalization of a positive-definite matrix
operator theory, a branch of mathematics, a positive-definite kernel is a generalization of a positive-definite function or a positive-definite matrix. It was
Positive-definite_kernel
Statistical method
In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso, LASSO or L1 regularization) is a regression analysis
Lasso_(statistics)
Topics referred to by the same term
set of vectors mapped to the zero vector Kernel (category theory), a generalization of the kernel of a homomorphism Kernel (set theory), an equivalence
Kernel
Representation learning method
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input
Sparse_dictionary_learning
Overview of and topical guide to deep learning
sets Generalization Overfitting Underfitting Hyperparameter Hyperparameter optimization Foundation model Large language model Supervised learning Unsupervised
Outline_of_deep_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
Software for understanding biological data
Machine learning in biology is the application of machine learning algorithms to biology, especially in computational biology and bioinformatics, including
Machine_learning_in_biology
Type of feedforward neural network
learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different
Convolutional_neural_network
Assumptions for inference in machine learning
and optimization Mitchell, T. M. (1980), The need for biases in learning generalizations, CBM-TR 5-110, New Brunswick, New Jersey, USA: Rutgers University
Inductive_bias
Theory and practice of education
understood as the approach to teaching, is the theory and practice of learning, and how this process influences, and is influenced by, the social, political
Pedagogy
Algorithm
GeneRec is a generalization of the recirculation algorithm, and approximates Almeida–Pineda recurrent backpropagation. It is used as part of the Leabra
GeneRec
Theory of education advocating a hands-on approach
the importance of learning by doing as a means of increasing productivity. In the article he writes that "one empirical generalization is so clear that
Learning-by-doing
Posits ability to interpolate within latent manifolds
The ability to interpolate between samples is the key to generalization in deep learning. An empirically-motivated approach to the manifold hypothesis
Manifold_hypothesis
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GENERALIZATION LEARNING
GENERALIZATION LEARNING
Girl/Female
Tamil
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Knowledge, Learning
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Girl/Female
Tamil
Vidhyavathi | விதà¯à®¯à®¾à®µà®¾à®¤à¯€
Wisdom, Knowledge, Learning, Goddess Durga
Vidhyavathi | விதà¯à®¯à®¾à®µà®¾à®¤à¯€
Girl/Female
Tamil
Goddess of learning, Saraswati
Girl/Female
Tamil
Goddess of learning, Saraswati
Boy/Male
Muslim
Perfect, Complete, Generalization
Girl/Female
Tamil
Vaagdevi | வாகà¯à®¤à¯‡à®µà¯€
Goddess of learning, Saraswati
Vaagdevi | வாகà¯à®¤à¯‡à®µà¯€
Girl/Female
Tamil
Saraswathy | ஸரஸà¯à®µà®¾à®¤à¯€ Â
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswathy | ஸரஸà¯à®µà®¾à®¤à¯€ Â
Boy/Male
Indian
Perfect, Complete, Generalization
Girl/Female
Tamil
Goddess of learning, Goddess Saraswati
Boy/Male
Tamil
Vidyasagar | விதà¯à®¯à®¾à®¸à®¾à®•à®°Â
Ocean of learning
Vidyasagar | விதà¯à®¯à®¾à®¸à®¾à®•à®°Â
Girl/Female
Tamil
Goddess of learning, Saraswati
Girl/Female
Tamil
Learning
Girl/Female
Tamil
Saraswati | ஸரஸà¯à®µà®¤à¯€
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswati | ஸரஸà¯à®µà®¤à¯€
Boy/Male
Indian
Perfect, Complete, Generalization
Girl/Female
Tamil
Vidyasri | விதà¯à®¯à®¾à®¸à®°à¯€
Wisdom, Knowledge, Learning, Goddess Durga
Vidyasri | விதà¯à®¯à®¾à®¸à®°à¯€
Surname or Lastname
English, French, German, Hungarian (Donát), Polish, and Czech (Donát)
English, French, German, Hungarian (Donát), Polish, and Czech (Donát) : from a medieval personal name (Latin Donatus, past participle of donare, frequentative of dare ‘to give’). The name was much favored by early Christians, either because the birth of a child was seen as a gift from God, or else because the child was in turn dedicated to God. The name was borne by various early saints, among them a 6th-century hermit of Sisteron and a 7th-century bishop of Besançon, all of whom contributed to the popularity of the baptismal name in the Middle Ages, which was not checked by the heresy of a 4th-century Carthaginian bishop who also bore it. Another bearer was a 4th-century gramMarian and commentator on Virgil, widely respected in the Middle Ages as a figure of great learning.
Boy/Male
Tamil
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Learning ocean
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Girl/Female
Tamil
Saraswathi | ஸரஸà¯à®µà®¾à®¤à¯€Â
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswathi | ஸரஸà¯à®µà®¾à®¤à¯€Â
Girl/Female
Tamil
Sarasvati | ஸரஸà¯à®µà®¤à¯€
A Goddess of learning
Sarasvati | ஸரஸà¯à®µà®¤à¯€
Boy/Male
Muslim
Perfect, Complete, Generalization
GENERALIZATION LEARNING
GENERALIZATION LEARNING
GENERALIZATION LEARNING
GENERALIZATION LEARNING
GENERALIZATION LEARNING
GENERALIZATION LEARNING
GENERALIZATION LEARNING
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