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machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that compare new problem instances with
Instance-based_learning
Broadbent's Sugar Production Factory task[clarification needed]. The Instance-Based Learning Theory (IBLT) is a theory of how humans make decisions in dynamic
Dynamic_decision-making
Type of supervised learning in machine learning
In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually
Multiple_instance_learning
Overview of and topical guide to machine learning
handling (GMDH) Inductive logic programming Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum
Outline_of_machine_learning
Use of technology in education to enhance learning and teaching
encompasses several domains, including learning theory, computer-based training, online learning, and mobile learning (m-learning). The Association for Educational
Educational_technology
Game genre
problem solving. Game-based learning (GBL) is a type of game play that has defined learning outcomes. Generally, game-based learning is designed to balance
Educational_game
pre-processing step that can be applied in many machine learning (or data mining) tasks. Approaches for instance selection can be applied for reducing the original
Instance_selection
Pedagogical approach
task-based learning processes. According to Jon Larsson, in considering problem-based learning for language learning, i.e., task-based language learning:
Task-based_language_teaching
Subset of artificial intelligence
learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based
Machine_learning
Machine learning paradigm
machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on
Supervised_learning
Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an
Explanation-based_learning
Process of acquiring new knowledge
learned. Evidence-based learning is the use of evidence from well designed scientific studies to accelerate learning. Evidence-based learning methods such
Learning
Topics referred to by the same term
IBLT may refer to: Instance-based learning theory, a theory of how humans make decisions Invertible Bloom lookup table, a probabilistic map data structure
IBLT
Property of a model
value of k leads to high bias and low variance (see below). In instance-based learning, regularization can be achieved varying the mixture of prototypes
Bias–variance_tradeoff
Canadian computer scientist (1944–2019)
included meander (art), compass and straightedge constructions, instance-based learning, music information retrieval, and computational music theory. He
Godfried_Toussaint
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
Term in educational psychology
in learning, decisions are made based on properties alone and rely on simple criteria that do not require a lot of memory. Example of rule-based theory:
Concept_learning
Memorization technique based on repetition
Rote learning is a memorization technique based on repetition. The method rests on the premise that the recall of repeated material becomes faster the
Rote_learning
Decentralized machine learning
of things, and pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets
Federated_learning
Machine learning technique
formulated the discriminability-based transfer (DBT) algorithm. By 1998, the field had advanced to include multi-task learning, along with more formal theoretical
Transfer_learning
Jinyan; et al. (2004). "Deeps: A new instance-based lazy discovery and classification system". Machine Learning. 54 (2): 99–124. doi:10.1023/b:mach.0000011804
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Type of machine learning method
k-NN technique, which is instance-based and function is only estimated locally. Theoretical disadvantages with lazy learning include: The large space
Lazy_learning
Optimization problem in computer science
Dimension reduction Fixed-radius near neighbors Fourier analysis Instance-based learning k-nearest neighbor algorithm Linear least squares Locality-sensitive
Nearest_neighbor_search
Subfield of machine learning
(model-based) learning effective distance metrics (metrics-based) explicitly optimizing model parameters for fast learning (optimization-based). Model-based
Meta-learning (computer science)
Meta-learning_(computer_science)
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Educational software application
automated recommendations for courses based on a user's skill profile as well as extract metadata from learning materials to make such recommendations
Learning_management_system
Machine learning strategy
datapoint. As contrasted with Pool-based sampling, the obvious drawback of stream-based methods is that the learning algorithm does not have sufficient
Active learning (machine learning)
Active_learning_(machine_learning)
Branch of machine learning
seen as low-quality models for that purpose. Most modern deep learning models are based on multi-layered neural networks such as convolutional neural
Deep_learning
1997 novel by Iain Pears
contrast portrayed in the novel is, on one hand, a philosophy based on ancient and medieval learning, and, on the other, the scientific method that was beginning
An_Instance_of_the_Fingerpost
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
Mexican-American scientist
theory of decision from experience in dynamic environments, called Instance-Based Learning Theory (IBLT). IBLT has been used as the basis to develop multiple
Cleotilde_Gonzalez
Natural-language understanding software
relational models, PRAC uses the principles of analogical reasoning and instance-based learning to infer completions of roles in semantic networks. PRAC has been
Probabilistic_Action_Cores
Non-parametric classification method
"Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining". International Journal of Computational Geometry
K-nearest_neighbors_algorithm
American machine learning company
based in New York City. Its online platform allows users to share datasets and machine learning models. It also develops open-source machine learning
Hugging_Face
Machine learning paradigm
learning more closely imitates the way humans learn to classify objects. During SSL, the model learns in two steps. First, the task is solved based on
Self-supervised_learning
"Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining", International Journal of Computational Geometry
Beta_skeleton
Paradigm in machine learning that uses no classification labels
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled
Unsupervised_learning
Topics referred to by the same term
InBound Links, a metric used by search engines Instance-based learning, a family of machine learning algorithms (e.g. KNN, PEL-C, IBL-1, IBL-2 and IBL-3)
IBL
K-Means – clustering algorithm based on minimizing within-cluster distances K-Nearest Neighbors (KNN) – instance-based learning and classification method Linear
List_of_data_science_software
Research field that lies at the intersection of machine learning and computer security
automatically craft binaries to evade learning-based detectors while preserving malicious functionality. Optimization-based attacks such as GAMMA use genetic
Adversarial_machine_learning
Software agent which acts autonomously
expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired
Intelligent_agent
British-American computer scientist
learning, artificial intelligence, robotics, and large-scale statistical data mining. He is the co-founder and CEO of Lovelace AI, a Pittsburgh-based
Andrew_W._Moore
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)
Computational model used in machine learning
late 1940s, D. O. Hebb proposed a learning hypothesis based on neural plasticity that became known as Hebbian learning. It was used in many early neural
Neural network (machine learning)
Neural_network_(machine_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
predict their class. The name is a play on words based on the earlier concept of one-shot learning in computer vision, in which classification can be
Zero-shot_learning
Concept in education and psychology
culture offers a unique approach to play and learning, emphasizing reality-based activities and observation. Learning through Play Yucatec Maya children engage
Learning_through_play
System to predict users' preferences
session-based recommendations are mainly based on generative sequential models such as recurrent neural networks, transformers, and other deep-learning-based
Recommender_system
Type of machine learning model
A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing
Large_language_model
Theories that aim to account for differences in individuals' learning
psychologists have argued that this "is not an instance of learning styles, rather, it is an instance of ability appearing as a style". Likewise, Fleming
Learning_styles
Learning that occurs through observing the behaviour of others
Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based on various
Observational_learning
Table layout for visualizing performance; also called an error matrix
sounds. In machine learning these matrices show the success of the learning system both in supervised learning and unsupervised learning, where they are
Confusion_matrix
Automated recognition of patterns and regularities in data
provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure then generates a model
Pattern_recognition
Machine learning framework
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was
Generative adversarial network
Generative_adversarial_network
Transmission of knowledge and skills
best available empirical evidence. It includes evidence-based teaching, evidence-based learning, and school effectiveness research. Autodidacticism, or
Education
Technique of inquiry-based learning
Discovery learning is a technique of inquiry-based learning and is considered a constructivist-based approach to education. It is also referred to as problem-based
Discovery_learning
Type of artificial neural network
t]−φ[x(t)]=x(t+1)−d(t+1). Radial basis function kernel instance-based learning In Situ Adaptive Tabulation Predictive analytics Chaos theory Hierarchical
Radial_basis_function_network
System to identify an author
performance. The machine learning algorithms that work well for author profiling on blogs include: Instance-based learning Random Decision Forests Email
Author_profiling
Educational learning method using computer algorithms and AI
courses, training programs, or learning and development programs. Adaptive learning systems have previously been used, for instance, to help students develop
Adaptive_learning
Type of feedforward neural network
network with two learning layers. Backpropagation was independently developed multiple times in early 1970s. The earliest instance was Seppo Linnainmaa's
Multilayer_perceptron
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
Tree-based machine learning method for classification
An alternating decision tree (ADTree) is a machine learning method for classification. It generalizes decision trees and has connections to boosting. An
Alternating_decision_tree
Categorization of data using statistics
possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features
Statistical_classification
Ongoing, voluntary, and self-motivated pursuit of knowledge
Lifelong learning is the "ongoing, voluntary, and self-motivated" pursuit of learning for either personal or professional reasons. Lifelong learning is important
Lifelong_learning
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
Set of methods for supervised statistical learning
Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and
Support_vector_machine
Range of neurodevelopmental conditions
Learning disability, or learning disorder, is a condition in the brain that causes difficulties comprehending or processing information and can be caused
Learning_disability
Creation of knowledge from structured and unstructured sources
databases into RDF, identity resolution, knowledge discovery and ontology learning. The general process uses traditional methods from information extraction
Knowledge_extraction
2002, see pp 45–47). Computational Linguistics Connectionism Instance-based learning k-nearest neighbor algorithm Royal Skousen (1989). Analogical Modeling
Analogical_modeling
AI whose outputs can be understood by humans
overlapping with interpretable AI, interpretable machine learning and explainable machine learning (XML), is a field of research that explores methods that
Explainable artificial intelligence
Explainable_artificial_intelligence
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
Defines whether uppercase and lowercase letters are treated as distinct
(case-sensitive) or equivalent (case-insensitive). For instance, when users interested in learning about dogs search an e-book, "dog" and "Dog" are of the
Case_sensitivity
Teaching by lecture or demonstration
inquiry-based learning. DI includes tutorials, participatory laboratory classes, discussions, recitation, seminars, workshops, observation, active learning,
Direct_instruction
Unintentional learning of complex information
implicit and explicit learning; for instance, research on amnesia often shows intact implicit learning but impaired explicit learning. Another difference
Implicit_learning
Optimization algorithm
from the mentioned link. It is based on a condition known as the Armijo–Goldstein condition. Both methods allow learning rates to change at each iteration;
Stochastic_gradient_descent
Educational support software
fully-hosted learning sites open to public courses and 350 other instances run by organizations of all sizes. The Open edX server-side software is based on Python
Open_edX
Classification problem where multiple labels may be assigned to each instance
constraint on how many of the classes the instance can be assigned to. The formulation of multi-label learning was first introduced by Shen et al. in the
Multi-label_classification
Framework for machine learning
statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such
Statistical_learning_theory
Interdisciplinary research area
Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks which
Quantum_machine_learning
Diffusion model over latent embedding space
widely used in practical diffusion models. For instance, Stable Diffusion versions 1.1 to 2.1 were based on the LDM architecture. Diffusion models were
Latent_diffusion_model
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
Fundamental unit of cognition
mechanisms include associative learning, in which similarities are gradually noticed as learners encounter instances, and hypothesis testing, which involves
Concept
Type of feedforward neural network
including text, images and audio. CNNs are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently
Convolutional_neural_network
Automatic creation of ontologies
extract relation signatures, often based on pattern-based or definition-based hypernym extraction techniques. Ontology learning (OL) is used to (semi-)automatically
Ontology_learning
Method in natural language processing
(PDF). Journal of Machine Learning Research. Qureshi, M. Atif; Greene, Derek (2018-06-04). "EVE: explainable vector based embedding technique using Wikipedia"
Word_embedding
Structuring text as input to generative artificial intelligence
model to perform in-context learning can be viewed as an instance of the more general learning-to-learn or meta-learning paradigm Quantifying Language
Prompt_engineering
Research field in deep learning
deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models
Topological_deep_learning
Subfield of machine learning
Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. Preference
Preference_learning
Cryptographic primitives that involve lattices
Kyber version as Module-Lattice-based Key Encapsulation Mechanism (ML-KEM). FrodoKEM, a scheme based on the learning with errors (LWE) problem. FrodoKEM
Lattice-based_cryptography
Relationship between proficiency and experience
a learning curve Proficiency (test score)Experience (hours spent)01234503691215Proficiency (test score)Example of a steep learning curve A learning curve
Learning_curve
Set of learning techniques in machine learning
In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations
Representation_learning
2026 information security incident
breach the infrastructure of Hugging Face, a platform hosting machine learning models and datasets. Contributing factors included a lack of log monitoring
OpenAI rogue agent breach of Hugging Face
OpenAI_rogue_agent_breach_of_Hugging_Face
Machine-learning process
More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings
Grammar_induction
Educational approach which arranges students into cooperative groups
Cooperative learning is an educational approach which aims to organize classroom activities into academic and social learning experiences. There is much
Cooperative_learning
Artificial neural network node function
smaller learning rates are typically necessary.[citation needed] Continuously differentiable This property is desirable for enabling gradient-based optimization
Activation_function
Computing paradigm based on information entities ("granules")
Ming (1994), Discretization of continuous-valued attributes and instance-based learning (Technical Report No.491), Sydney: Basser Department of Computer
Granular_computing
Measurement of algorithmic bias
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions
Fairness_(machine_learning)
Theory of knowledge
problem-based learning and inquiry learning may be ineffective. They described several research studies that were favorable to problem-based learning given
Constructivism (philosophy of education)
Constructivism_(philosophy_of_education)
Class of artificial neural network
expensive online variant is called "Real-Time Recurrent Learning" or RTRL, which is an instance of automatic differentiation in the forward accumulation
Recurrent_neural_network
Theory and practice of education
Evidence-based education is related to evidence-based teaching, evidence-based learning, and school effectiveness research. Dialogic learning is learning that
Pedagogy
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INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
Girl/Female
British, English
Based
Girl/Female
American, Australian, British, Christian, Dutch, English, French, German, Latin, Portuguese, Shakespearean, Swedish
Constancy; Steadfastness
Boy/Male
Muslim
Smiling
Female
French
French form of Latin Constantia, CUSTANCE means "steadfast."Â
Girl/Female
Arabic, Muslim
Example; Instance; Precedent
Boy/Male
Arabic
Distance
Girl/Female
Hindu
Insurance
Boy/Male
Muslim/Islamic
Brave
Boy/Male
Arabic, Australian
Smiling
Surname or Lastname
English and French
English and French : from the medieval female personal name Constance, Latin Constantia, originally a feminine form of Constantius (see Constant), but later taken as the abstract noun constantia ‘steadfastness’.English and French : habitational name from Coutances in La Manche, France, which was named Constantia in Latin (see above) in honor of the Roman emperor Constantius Chlorus, who was responsible for fortifying the settlement in ad 305.
Boy/Male
Muslim/Islamic
Smiling
Female
English
Variant spelling of English/Scottish Anstice, ANSTACE means "resurrection."
Boy/Male
Arabic, French, Hindu, Indian, Marathi, Muslim, Sindhi
Joy; Solved; Based
Boy/Male
Indian
Distance
Female
English
English form of Latin Constantia, CONSTANCE means "steadfast."Â
Girl/Female
Latin American English French Shakespearean
Firm of purpose. Constancy, from the Latin Constantia.
Girl/Female
Greek
One who will be reborn.
Girl/Female
Tamil
Insurance
Male
Egyptian
, the father of Hor-imhotep.
Boy/Male
Afghan, African, Arabic, Australian, Chinese, Greek, Indian, Muslim
Brave
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
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