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In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit
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
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
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
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
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
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
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
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
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)
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
Optimization problem in computer science
Content-based image retrieval Curse of dimensionality Digital signal processing Dimension reduction Fixed-radius near neighbors Fourier analysis Instance-based
Nearest_neighbor_search
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
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
Educational software application
of distance learning. This is the first known instance of the use of materials for independent language study. The concept of e-learning began to develop
Learning_management_system
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
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
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
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
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
1997 novel by Iain Pears
An Instance of the Fingerpost is a 1997 historical mystery novel by Iain Pears. The main setting is Oxford in 1663, with the events initially revolving
An_Instance_of_the_Fingerpost
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
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
Computerized information extraction from images
feature-based methods used in conjunction with machine learning techniques and complex optimization frameworks. The advancement of Deep Learning techniques
Computer_vision
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
Type of artificial neural network
problems related to the sigmoids. Learning occurs by changing connection weights after each piece of data is processed, based on the amount of error in the
Feedforward_neural_network
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
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
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
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
Term in educational psychology
Concept learning, also known as category learning, concept attainment, and concept formation, is defined by Bruner, Goodnow, & Austin (1956) as "the search
Concept_learning
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
Topics referred to by the same term
Image-based lighting, an image rendering technique Inbred backcross lines, a breeding technique InBound Links, a metric used by search engines Instance-based
IBL
Concept in education and psychology
towards learning without rushing them. Incorporating Objects Adults introduce new objects during play to spark children's curiosity. For instance, they
Learning_through_play
Largely debunked 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
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
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
"Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining", International Journal of Computational Geometry
Beta_skeleton
Tree-based machine learning method for classification
predicate condition, and prediction nodes, which contain a single number. An instance is classified by an ADTree by following all paths for which all decision
Alternating_decision_tree
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)
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
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
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
Paradigm in machine learning that uses no classification labels
are added on to enable new capabilities or removed to make learning faster. For instance, neurons change between deterministic (Hopfield) and stochastic
Unsupervised_learning
Range of neurodevelopmental conditions
Learning disability, primarily learning disorder, or learning difficulty (British English) is a condition in the brain that causes difficulties comprehending
Learning_disability
Type of associative learning process for behavioral modification
even longer delay before behavior extinction due to the learning factor of repeated instances becoming necessary to get reinforcement, when compared with
Operant_conditioning
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)
Meta-algorithmic technique to choose an algorithm
classification problem by learning a mapping f i ↦ A {\displaystyle f_{i}\mapsto {\mathcal {A}}} for a given instance i {\displaystyle i} . Instance features are numerical
Algorithm_selection
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
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
Programming which all objects are created by classes
since. Its creation was based in similar concept as block used in prior-based ALGOL 68 programming language. As an instance of a class, an object is
Class_(programming)
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
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
Type of machine learning model
model's predictions are based on the properties of sequences within its training dataset. A mixture of experts (MoE) is a machine learning architecture in which
Large_language_model
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)
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
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
Type of artificial neural network
t]-\varphi [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 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
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
Fundamental unit of cognition
mechanisms include associative learning, in which similarities are gradually noticed as learners encounter instances, and hypothesis testing, which involves
Concept
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
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
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
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
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
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
Software agent which acts autonomously
autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge.[citation needed] AI textbooks[which?] define
Intelligent_agent
2018 video game
Baldi's Basics in Education and Learning is a 2018 survival horror video game developed and published by Micah McGonigal. Parodying 1990s educational games;
Baldi's Basics in Education and Learning
Baldi's_Basics_in_Education_and_Learning
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
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 mathematical analysis of machine learning
computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed
Probably approximately correct learning
Probably_approximately_correct_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)
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
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
Aspect of learning procedure
classical conditioning from other forms of associative learning (e.g., instrumental learning and human associative memory); a number of observations
Classical_conditioning
Function that is tied to a particular instance or class
time) would be a property. In class-based programming, methods are defined within a class, and objects are instances of a given class. One of the most important
Method_(computer_programming)
Vector quantization algorithm minimizing the sum of squared deviations
grouped together. For instance, a retail company may use k-means clustering to segment its customer base into distinct groups based on factors such as purchasing
K-means_clustering
multi-objective optimization problem. Cost-sensitive machine learning optimizes models based on the specific consequences of misclassifications, making
Cost-sensitive machine learning
Cost-sensitive_machine_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
Flaw in mathematical modelling
possible to reconstruct details of individual training instances from an overfitted machine learning model's training set. This may be undesirable if, for
Overfitting
Data model
Learning Object Metadata is a data model, usually encoded in XML, used to describe a learning object and similar digital resources used to support learning
Learning_object_metadata
Piece of information about the content of an image
to a certain application. This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated
Feature_(computer_vision)
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
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
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
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 machine learning method
Bosch, Antal (October 2005). "Hybrid algorithms with Instance-Based Classification". Machine Learning: ECML2005. Springer. pp. 158–169. ISBN 9783540292432
Eager_learning
Suite of machine learning software written in Java
based on deep learning techniques written in C++. Orange is a similar open-source project for data mining, machine learning and visualization based on
Weka_(software)
Distance education using mobile device technology
M-learning, or mobile learning, is a form of distance education or technology enhanced active learning where learners use portable devices such as mobile
M-learning
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
Type of feedforward neural network
network with two learning layers. Backpropagation was independently developed multiple times in early 1970s. The earliest published instance was Seppo Linnainmaa's
Multilayer_perceptron
Transmission of knowledge and skills
productive learning process. Different subjects frequently use different approaches; for instance, language education often focuses on verbal learning, while
Education
Pattern-recognition performance metrics
instances}}{{\text{All }}{\textbf {relevant}}{\text{ instances}}}}} Both precision and recall are therefore based on relevance. Consider a computer program for
Precision_and_recall
Statistical machine learning algorithm for metric learning
The goal of supervised learning (more specifically classification) is to learn a decision rule that can categorize data instances into pre-defined classes
Large_margin_nearest_neighbor
Process of learning better perception skills
in some cases, there is an overlap between perceptual learning and category learning. For instance, to discriminate between two items, a categorical difference
Perceptual_learning
AI whose outputs can be understood by humans
are based on. This makes it possible to confirm existing knowledge, challenge existing knowledge, and generate new assumptions. Machine learning (ML)
Explainable artificial intelligence
Explainable_artificial_intelligence
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
Boy/Male
Afghan, African, Arabic, Australian, Chinese, Greek, Indian, Muslim
Brave
Boy/Male
Muslim/Islamic
Smiling
Boy/Male
Arabic, Australian
Smiling
Female
French
French form of Latin Constantia, CUSTANCE means "steadfast."Â
Boy/Male
Muslim/Islamic
Brave
Girl/Female
Tamil
Insurance
Girl/Female
American, Australian, British, Christian, Dutch, English, French, German, Latin, Portuguese, Shakespearean, Swedish
Constancy; Steadfastness
Female
English
English form of Latin Constantia, CONSTANCE means "steadfast."Â
Boy/Male
Arabic
Distance
Girl/Female
British, English
Based
Male
Egyptian
, the father of Hor-imhotep.
Girl/Female
Latin American English French Shakespearean
Firm of purpose. Constancy, from the Latin Constantia.
Girl/Female
Greek
One who will be reborn.
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.
Girl/Female
Hindu
Insurance
Boy/Male
Arabic, French, Hindu, Indian, Marathi, Muslim, Sindhi
Joy; Solved; Based
Boy/Male
Muslim
Smiling
Boy/Male
Indian
Distance
Female
English
Variant spelling of English/Scottish Anstice, ANSTACE means "resurrection."
Girl/Female
Arabic, Muslim
Example; Instance; Precedent
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
Girl/Female
Muslim
Of Ambergris.
Girl/Female
British, English, Latin
Joy; Gladness
Boy/Male
Muslim
Poor. Sufi mendicant.
Boy/Male
Arabic, Australian, Indian, Parsi
A Sword; Pond; Pool
Girl/Female
American, Australian, Christian, Dutch, French, Greek
Pearl
Girl/Female
American, British, Christian, English, German, Greek
Pure; Form of Catherine
Boy/Male
British, English
Son of Little will
Boy/Male
Russian
Male
Yiddish
Yiddish form of Hebrew Yechezqel, HASKEL means "God will strengthen."
Girl/Female
Hindu, Indian
Like Shree
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
INSTANCE BASED-LEARNING
n.
The act or quality of being instant or pressing; urgency; solicitation; application; suggestion; motion.
imp. & p. p.
of Base
v. t.
To outstrip by as much as a distance (see Distance, n., 3); to leave far behind; to surpass greatly.
imp. & p. p.
of Instance
a.
Immediately; instantly; at once; as, he left instanter.
a.
Reduced; lowered; restrained; as, to speak with bated breath.
v. t.
To mention as a case or example; to refer to; to cite; as, to instance a fact.
a.
Morally low. Hence: Low-minded; unworthy; without dignity of sentiment; ignoble; mean; illiberal; menial; as, a base fellow; base motives; base occupations.
v. t.
To place at a distance or remotely.
n.
The act of issuing, or giving out; as, the issuance of an order; the issuance of rations, and the like.
v. t.
To impress, as an animating power, or instinct.
a.
Alloyed with inferior metal; debased; as, base coin; base bullion.
a.
A day of the present or current month; as, the sixth instant; -- an elliptical expression equivalent to the sixth of the month instant, i. e., the current month. See Instant, a., 3.
n.
Wearing, or protected by, bases.
n.
A rustic play; -- called also prisoner's base, prison base, or bars.
n.
That which is instant or urgent; motive.
a.
Used by, or appropriated to, insane persons; as, an insane hospital.
n.
Instance; urgency.
a.
Having a base, or having as a base; supported; as, broad-based.
a.
A natural aptitude or knack; a predilection; as, an instinct for order; to be modest by instinct.