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Instatistics, thegrouped Dirichlet distribution(GDD) is a multivariate generalization of theDirichlet distributionIt was first described by Ng et al. 2008.[1]The Grouped Dirichlet distribution arises in the analysis ofcategorical datawhere some observations could fall into any of a set of other 'crisp' category. For e... | https://en.wikipedia.org/wiki/Grouped_Dirichlet_distribution |
Instatistics, theinverted Dirichlet distributionis a multivariate generalization of thebeta prime distribution, and is related to theDirichlet distribution. It was first described by Tiao and Cuttman in 1965.[1]
The distribution has a density function given by
The distribution has applications instatistical regress... | https://en.wikipedia.org/wiki/Inverted_Dirichlet_distribution |
Inprobability theory,Dirichlet processes(after the distribution associated withPeter Gustav Lejeune Dirichlet) are a family ofstochastic processeswhoserealizationsareprobability distributions. In other words, a Dirichlet process is a probability distribution whose range is itself a set of probability distributions. It ... | https://en.wikipedia.org/wiki/Dirichlet_process |
Instatistics, thematrix variate Dirichlet distributionis a generalization of thematrix variate beta distributionand of theDirichlet distribution.
SupposeU1,…,Ur{\displaystyle U_{1},\ldots ,U_{r}}arep×p{\displaystyle p\times p}positive definite matriceswithIp−∑i=1rUi{\displaystyle I_{p}-\sum _{i=1}^{r}U_{i}}also positi... | https://en.wikipedia.org/wiki/Matrix_variate_Dirichlet_distribution |
Thepartition functionorconfiguration integral, as used inprobability theory,information theoryanddynamical systems, is a generalization of the definition of apartition function in statistical mechanics. It is a special case of anormalizing constantin probability theory, for theBoltzmann distribution. The partition func... | https://en.wikipedia.org/wiki/Partition_function_(mathematics) |
Inquantum field theory,partition functionsaregenerating functionalsforcorrelation functions, making them key objects of study in thepath integral formalism. They are theimaginary timeversions ofstatistical mechanicspartition functions, giving rise to a close connection between these two areas of physics. Partition func... | https://en.wikipedia.org/wiki/Partition_function_(quantum_field_theory) |
Inmechanics, thevirial theoremprovides a general equation that relates the average over time of the totalkinetic energyof a stable system of discrete particles, bound by aconservative force(where theworkdone is independent of path), with that of the totalpotential energyof the system. Mathematically, thetheoremstates t... | https://en.wikipedia.org/wiki/Virial_theorem |
TheWidom insertion methodis astatistical thermodynamicapproach to the calculation of material and mixture properties. It is named forBenjamin Widom, who derived it in 1963.[1]In general, there are two theoretical approaches to determining the statistical mechanical properties of materials. The first is the direct cal... | https://en.wikipedia.org/wiki/Widom_insertion_method |
In numerical analysis andcomputational statistics,rejection samplingis a basic technique used to generate observations from adistribution. It is also commonly called theacceptance-rejection methodor "accept-reject algorithm" and is a type of exact simulation method. The method works for any distribution inRm{\displayst... | https://en.wikipedia.org/wiki/Rejection_sampling |
Inactuarial science, theEsscher transform(Gerber & Shiu 1994) is a transform that takes aprobability densityf(x) and transforms it to a new probability densityf(x;h) with a parameterh. It was introduced by F. Esscher in 1932 (Esscher 1932).
Letf(x) be a probability density. Its Esscher transform is defined as
More ge... | https://en.wikipedia.org/wiki/Esscher_transform |
Anautonomous agentis anartificial intelligence(AI) system that can perform complex tasks independently.[1]
There are various definitions of autonomous agent. According to Brustoloni (1991):
"Autonomous agents are systems capable of autonomous, purposeful action in the real world."[2]
According to Maes (1995):
"Auto... | https://en.wikipedia.org/wiki/Autonomous_agent |
Biologically InspiredCognitive Architectures(BICA) was aDARPAproject administered by theInformation Processing Technology Office(IPTO). BICA began in 2005 and is designed to create the next generation ofcognitive architecturemodels of human artificial intelligence. Its first phase (Design) ran from September 2005 to a... | https://en.wikipedia.org/wiki/Biologically_inspired_cognitive_architectures |
Understanding how the brain works is arguably one of the greatest scientific challenges of our time.
TheWhite HouseBRAIN Initiative(Brain Research through Advancing Innovative Neurotechnologies) is a collaborative, public-private research initiative announced by theObama administrationon April 2, 2013, with the goal o... | https://en.wikipedia.org/wiki/BRAIN_Initiative |
The following table comparescognitive architectures. | https://en.wikipedia.org/wiki/Cognitive_architecture_comparison |
Cognitive computingrefers totechnology platformsthat, broadly speaking, are based on the scientific disciplines ofartificial intelligenceandsignal processing. These platforms encompassmachine learning,reasoning,natural language processing,speech recognitionandvision(object recognition),human–computer interaction,dialo... | https://en.wikipedia.org/wiki/Cognitive_computing |
Google Brainwas adeep learningartificial intelligenceresearch team that served as the sole AI branch of Google before being incorporated under the newer umbrella ofGoogle AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, it combined open-ended machine learning research with informa... | https://en.wikipedia.org/wiki/Google_Brain |
Inartificial intelligence, knowledge-based agents draw on a pool of logical sentences to infer conclusions about theworld. At theknowledge level, we only need to specify what the agent knows and what its goals are; a logical abstraction separate from details of implementation.
This notion of knowledge level was first... | https://en.wikipedia.org/wiki/Knowledge_level |
TheModular Cognition Framework(MCF) is an open-ended theoretical framework for research into the way the mind is organized. It draws on the common ground shared by contemporary research in the various areas that are collectively known ascognitive scienceand is designed to be applicable to all these fields of research. ... | https://en.wikipedia.org/wiki/Modular_Cognition_Framework |
Theneural correlates ofconsciousness(NCC) are the minimal set of neuronal events and mechanisms sufficient for the occurrence of themental statesto which they are related.[2]Neuroscientistsuseempirical approachesto discoverneural correlatesof subjective phenomena; that is, neural changes which necessarily and regularly... | https://en.wikipedia.org/wiki/Neural_correlates_of_consciousness |
Pandemonium architectureis a theory incognitive sciencethat describes how visual images are processed by the brain. It has applications inartificial intelligenceandpattern recognition. The theory was developed by the artificial intelligence pioneerOliver Selfridgein 1959. It describes the process of object recognition ... | https://en.wikipedia.org/wiki/Pandemonium_architecture |
Unified Theories of Cognitionis a 1990 book byAllen Newell.[1]Newell argues for the need of a set of generalassumptionsforcognitive modelsthat account for all of cognition: a unifiedtheoryofcognition, orcognitive architecture. The research started by Newell on unified theories of cognition represents a crucial element... | https://en.wikipedia.org/wiki/Unified_theory_of_cognition |
Never-Ending Language Learningsystem (NELL) is asemanticmachine learningsystemthat as of 2010 was being developed by a research team atCarnegie Mellon University, and supported by grants fromDARPA,Google,NSF, andCNPqwith portions of the system running on asupercomputingclusterprovided byYahoo!.[1]
NELL was programmed ... | https://en.wikipedia.org/wiki/Never-Ending_Language_Learning |
Open Mind Common Sense(OMCS) is anartificial intelligenceproject based at theMassachusetts Institute of Technology(MIT)Media Labwhose goal is to build and utilize a largecommonsense knowledge basefrom the contributions of many thousands of people across the Web. It has been active from 1999 to 2016.
Since its founding... | https://en.wikipedia.org/wiki/Open_Mind_Common_Sense |
Dynamic functional connectivity(DFC) refers to the observed phenomenon thatfunctional connectivitychanges over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a vari... | https://en.wikipedia.org/wiki/Dynamic_functional_connectivity |
Functional connectivitysoftware is used to study functional properties of theconnectomeusingfunctional Magnetic Resonance Imaging (fMRI)data in theresting stateand during tasks. To access many of these software applications visit theNIHfundedNeuroimaging Informatics Tools and Resources Clearinghouse (NITRC)site. | https://en.wikipedia.org/wiki/List_of_functional_connectivity_software |
TheHuman Connectome Project(HCP) was a five-year project (later extended to 10 years) sponsored by sixteen components of theNational Institutes of Health, split between two consortia of research institutions. The project was launched in July 2009[1]as the first of three Grand Challenges of the NIH's Blueprint for Neuro... | https://en.wikipedia.org/wiki/Human_Connectome_Project |
TheBudapest Reference Connectomeserver computes the frequently appearing anatomical brain connections of 418 healthy subjects.[1][2]It has been prepared fromdiffusion MRIdatasets of theHuman Connectome Projectinto a reference connectome (or braingraph), which can be downloaded in CSV and GraphML formats and visualized ... | https://en.wikipedia.org/wiki/Budapest_Reference_Connectome |
ADrosophilaconnectomeis a list ofneuronsin theDrosophila melanogaster(fruit fly) nervous system, and the chemicalsynapsesbetween them. The fly's central nervous system consists of the brain plus theventral nerve cord, and both are known to differ considerably between male and female.[1][2]Dense connectomes have been co... | https://en.wikipedia.org/wiki/Drosophila_connectome |
Biomimeticsorbiomimicryis the emulation of the models, systems, and elements of nature for the purpose of solving complexhumanproblems.[2][3][4]The terms "biomimetics" and "biomimicry" are derived fromAncient Greek:βίος(bios), life, and μίμησις (mīmēsis), imitation, from μιμεῖσθαι (mīmeisthai), to imitate, from μῖμος (... | https://en.wikipedia.org/wiki/Biomimicry |
Digital architecturerefers to aspects of architecture that featuredigitaltechnologies or considers digital platforms as online spaces. The emerging field of digital architectures therefore applies to both classic architecture as well as the emerging study of social media technologies.
Within classic architectural stud... | https://en.wikipedia.org/wiki/Digital_architecture |
Blobitecture(fromblob architecture),blobismandblobismusare terms for a movement inarchitecturein which buildings have an organic,amoeba-shaped building form.[1]Though the termblob architecturewas already in vogue in the mid-1990s, the wordblobitecturefirst appeared in print in 2002, inWilliam Safire's "On Language" col... | https://en.wikipedia.org/wiki/Blobitecture |
Generative artispost-conceptual artthat has been created (in whole or in part) with the use of anautonomoussystem. Anautonomous systemin this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In some case... | https://en.wikipedia.org/wiki/Generative_art |
Evolutionary artis a branch ofgenerative art, in which the artist does not do the work of constructing the artwork, but rather lets a system do the construction. In evolutionary art, initially generated art is put through an iterated process of selection and modification to arrive at a final product, where it is the a... | https://en.wikipedia.org/wiki/Evolutionary_art |
Elmo(stylized aselmo, ablendofelasticandmonkey) is acomputer shogievaluation function andbookfile (joseki) created by Makoto Takizawa (瀧澤誠). It is designed to be used with a third-party shogi alpha–beta search engine.
Combined with theyaneura ou(やねうら王) search, Elmo became the champion of the 27th annual World Computer... | https://en.wikipedia.org/wiki/Elmo_(shogi_engine) |
Stockfishis afree and open-sourcechess engine, available for various desktop and mobile platforms. It can be used inchess softwarethrough theUniversal Chess Interface.
Stockfish has been one of the strongest chess engines in the world for several years;[3][4][5]it has won all main events of theTop Chess Engine Champio... | https://en.wikipedia.org/wiki/Stockfish_chess_engine |
Chess softwarecomes in different forms. A chess playing program provides a graphical chessboard on which one can play a chess game against a computer. Such programs are available forpersonal computers,video game consoles,smartphones/tablet computersormainframes/supercomputers. Achess enginegenerates moves, but is acces... | https://en.wikipedia.org/wiki/List_of_chess_software |
This is a list ofgenetic algorithm(GA) applications. | https://en.wikipedia.org/wiki/List_of_genetic_algorithm_applications |
Particle filters, also known assequential Monte Carlomethods, are a set ofMonte Carloalgorithms used to find approximate solutions forfiltering problemsfor nonlinear state-space systems, such assignal processingandBayesian statistical inference.[1]Thefiltering problemconsists of estimating the internal states indynamic... | https://en.wikipedia.org/wiki/Particle_filter |
Aschema(pl.:schemata) is a template incomputer scienceused in the field ofgenetic algorithmsthat identifies asubsetof strings with similarities at certain string positions. Schemata are a special case ofcylinder sets, forming abasisfor aproduct topologyon strings.[1]In other words, schemata can be used to generate atop... | https://en.wikipedia.org/wiki/Propagation_of_schema |
Universal Darwinism, also known asgeneralized Darwinism,universal selection theory,[1]orDarwinian metaphysics,[2][3][4]is a variety of approaches that extend the theory ofDarwinismbeyond its original domain ofbiological evolutionon Earth. Universal Darwinism aims to formulate a generalized version of the mechanisms ofv... | https://en.wikipedia.org/wiki/Universal_Darwinism |
Incomputer scienceandmathematical optimization, ametaheuristicis a higher-levelprocedureorheuristicdesigned to find, generate, tune, or select a heuristic (partialsearch algorithm) that may provide a sufficiently good solution to anoptimization problemor amachine learningproblem, especially with incomplete or imperfect... | https://en.wikipedia.org/wiki/Metaheuristics |
The followingoutlineis provided as an overview of and topical guide to computer vision:
Computer vision–interdisciplinary fieldthat deals with how computers can be made to gain high-level understanding fromdigital imagesorvideos. From the perspective ofengineering, it seeks to automate tasks that the human visual syst... | https://en.wikipedia.org/wiki/Outline_of_computer_vision |
Thefollowingoutlineis provided as an overview of and topical guide to robotics:
Roboticsis a branch of mechanical engineering, electrical engineering and computer science that deals with the design, construction, operation, and application of robots, as well as computer systems for their control, sensory feedback, a... | https://en.wikipedia.org/wiki/Outline_of_robotics |
Theaccuracy paradoxis theparadoxicalfinding thataccuracyis not a good metric forpredictive modelswhenclassifyinginpredictive analytics. This is because a simple model may have a high level of accuracy but too crude to be useful. For example, if the incidence of category A is dominant, being found in 99% of cases, the... | https://en.wikipedia.org/wiki/Accuracy_paradox |
Action model learning(sometimes abbreviatedaction learning) is an area ofmachine learningconcerned with the creation and modification of asoftware agent's knowledge about theeffectsandpreconditionsof theactionsthat can be executed within itsenvironment. This knowledge is usually represented in a logic-basedaction descr... | https://en.wikipedia.org/wiki/Action_model_learning |
Activity recognitionaims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of severalcomputer sciencecommunities due to its strength in providing personalized sup... | https://en.wikipedia.org/wiki/Activity_recognition |
Anadaptive neuro-fuzzy inference systemoradaptive network-based fuzzy inference system(ANFIS) is a kind ofartificial neural networkthat is based onTakagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s.[1][2]Since it integrates both neural networks andfuzzy logicprinciples, it has potentia... | https://en.wikipedia.org/wiki/Adaptive_neuro_fuzzy_inference_system |
Adaptive resonance theory(ART) is a theory developed byStephen GrossbergandGail Carpenteron aspects of how the brainprocesses information. It describes a number ofartificial neural networkmodels which usesupervisedandunsupervised learningmethods, and address problems such aspattern recognitionand prediction.
The prima... | https://en.wikipedia.org/wiki/Adaptive_resonance_theory |
Inprobability theoryandinformation theory,adjusted mutual information, a variation ofmutual informationmay be used for comparingclusterings.[1]It corrects the effect of agreement solely due to chance between clusterings, similar to the way theadjusted rand indexcorrects theRand index. It is closely related tovariation ... | https://en.wikipedia.org/wiki/Adjusted_mutual_information |
AIVA(Artificial Intelligence Virtual Artist) is anelectronic composerrecognized by theSACEM.
Created in February 2016, AIVA specializes inclassicalandsymphonic musiccomposition.[1][2]It became the world's first virtual composer to be recognized by a music society (SACEM).[3][4]By reading a large collection of existing... | https://en.wikipedia.org/wiki/AIVA |
AIXI/ˈaɪksi/is a theoreticalmathematical formalismforartificial general intelligence.
It combinesSolomonoff inductionwithsequential decision theory.
AIXI was first proposed byMarcus Hutterin 2000[1]and several results regarding AIXI are proved in Hutter's 2005 bookUniversal Artificial Intelligence.[2]
AIXI is areinfor... | https://en.wikipedia.org/wiki/AIXI |
AlchemyAPIwas a software company in the field ofmachine learning. Its technology employeddeep learningfor various applications innatural language processing, such as semantic text analysis and sentiment analysis, as well ascomputer vision. AlchemyAPI offered both traditionally-licensed software products as well API acc... | https://en.wikipedia.org/wiki/AlchemyAPI |
Algorithm selection(sometimes also calledper-instance algorithm selectionoroffline algorithm selection) is a meta-algorithmic techniqueto choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algorithms have different perform... | https://en.wikipedia.org/wiki/Algorithm_selection |
Algorithmic inferencegathers new developments in thestatistical inferencemethods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field arecomputational learning theory,granular computing,bioinformatics, and, long ago, structural probability (Fraser 1966).
The m... | https://en.wikipedia.org/wiki/Algorithmic_inference |
Algorithmic learning theoryis a mathematical framework for analyzingmachine learningproblems and algorithms. Synonyms includeformal learning theoryandalgorithmic inductive inference[citation needed]. Algorithmic learning theory is different fromstatistical learning theoryin that it does not make use of statistical assu... | https://en.wikipedia.org/wiki/Algorithmic_learning_theory |
AlphaGois acomputer programthat plays theboard gameGo.[1]It was developed by the London-basedDeepMindTechnologies,[2]an acquired subsidiary ofGoogle. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the nameMaster.[3]After retiring from competitive play, AlphaGo Maste... | https://en.wikipedia.org/wiki/AlphaGo |
AlphaGo Zerois a version ofDeepMind'sGo softwareAlphaGo. AlphaGo's team published an article inNaturein October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version.[1]By playing games against itself, AlphaGo Zero: surpassed the strength ofAlphaGo ... | https://en.wikipedia.org/wiki/AlphaGo_Zero |
Inartificial intelligence,apprenticeship learning(orlearning from demonstrationorimitation learning) is the process of learning by observing an expert.[1][2]It can be viewed as a form ofsupervised learning, where the training dataset consists of task executions by a demonstration teacher.[2]
Mapping methods try to mim... | https://en.wikipedia.org/wiki/Apprenticeship_learning |
TheMarkov condition, sometimes called theMarkov assumption, is an assumption made inBayesian probability theory, that every node in aBayesian networkisconditionally independentof its nondescendants, given its parents. Stated loosely, it is assumed that a node has no bearing on nodes which do not descend from it. In aDA... | https://en.wikipedia.org/wiki/Causal_Markov_condition |
Competitive learningis a form ofunsupervised learninginartificial neural networks, in which nodes compete for the right to respond to a subset of the input data.[1][2]A variant ofHebbian learning, competitive learning works by increasing the specialization of each node in the network. It is well suited to findingclust... | https://en.wikipedia.org/wiki/Competitive_learning |
Concept learning, also known ascategory learning,concept attainment, andconcept formation, is defined byBruner, Goodnow, & Austin (1956) as "the search for and testing of attributes that can be used to distinguish exemplars from non exemplars of various categories".[a]More simply put, concepts are the mental categories... | https://en.wikipedia.org/wiki/Concept_learning |
Thedistributional learning theoryorlearning of probability distributionis a framework incomputational learning theory. It has been proposed fromMichael Kearns,Yishay Mansour,Dana Ron,Ronitt Rubinfeld,Robert SchapireandLinda Selliein 1994[1]and it was inspired from thePAC-frameworkintroduced byLeslie Valiant.[2]
In thi... | https://en.wikipedia.org/wiki/Distribution_learning_theory |
Inartificial intelligence,eager learningis a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed tolazy learning, where generalization beyond the training data is delayed until a query is made to the system.[1]The main advantage g... | https://en.wikipedia.org/wiki/Eager_learning |
Deep reinforcement learning(DRL) is a subfield ofmachine learningthat combines principles ofreinforcement learning(RL) anddeep learning. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while usingdeep neural networksto represent policies, value functions,... | https://en.wikipedia.org/wiki/End-to-end_reinforcement_learning |
InPAC learning,error tolerancerefers to the ability of analgorithmto learn when the examples received have been corrupted in some way. In fact, this is a very common and important issue since in many applications it is not possible to access noise-free data. Noise can interfere with the learning process at different le... | https://en.wikipedia.org/wiki/Error_tolerance_(PAC_learning) |
Inmachine learningandpattern recognition, afeatureis an individual measurable property or characteristic of a data set.[1]Choosing informative, discriminating, and independent features is crucial to produce effectivealgorithmsforpattern recognition,classification, andregressiontasks. Features are usually numeric, but o... | https://en.wikipedia.org/wiki/Feature_(machine_learning) |
Inferential Theory of Learning(ITL) is an area ofmachine learningwhich describes inferential processes performed by learning agents. ITL has been continuously developed byRyszard S. Michalski, starting in the 1980s. The first known publication of ITL was in 1983.[1]In the ITLlearning processis viewed as a search (infer... | https://en.wikipedia.org/wiki/Inferential_theory_of_learning |
Alearning automatonis one type ofmachine learningalgorithm studied since 1970s. Learning automata select their current action based on past experiences from the environment. It will fall into the range of reinforcement learning if the environment isstochasticand aMarkov decision process(MDP) is used.
Research in learn... | https://en.wikipedia.org/wiki/Learning_automata |
Anartificial neural network'slearning ruleorlearning processis a method, mathematical logic oralgorithmwhich improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating theweight and bias[broken anchor]levels of a network when it is simulate... | https://en.wikipedia.org/wiki/Learning_rule |
Incryptography,learning with errors(LWE) is a mathematical problem that is widely used to create secureencryption algorithms.[1]It is based on the idea of representing secret information as a set of equations with errors. In other words, LWE is a way to hide the value of a secret by introducing noise to it.[2]In more t... | https://en.wikipedia.org/wiki/Learning_with_errors |
Inmachine learningandcomputer vision,M-theoryis a learning framework inspired by feed-forward processing in theventral streamofvisual cortexand originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas, such asspeech recognition. On certain image reco... | https://en.wikipedia.org/wiki/M-Theory_(learning_framework) |
Machine learning control(MLC) is a subfield ofmachine learning,intelligent control, andcontrol theorywhich aims to solveoptimal controlproblems with machine learning methods. Key applications are complex nonlinear systems for whichlinear control theorymethods are not applicable.
Four types of problems are commonly enc... | https://en.wikipedia.org/wiki/Machine_learning_control |
Machine learning in bioinformaticsis the application ofmachine learningalgorithms tobioinformatics,[1]includinggenomics,proteomics,microarrays,systems biology,evolution, andtext mining.[2][3]
Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such asprotein... | https://en.wikipedia.org/wiki/Machine_learning_in_bioinformatics |
Inmachine learning, themarginof a single data point is defined to be the distance from the data point to adecision boundary. Note that there are many distances and decision boundaries that may be appropriate for certain datasets and goals. Amargin classifieris aclassificationmodel that utilizes the margin of each examp... | https://en.wikipedia.org/wiki/Margin_(machine_learning) |
AMarkov logic network(MLN) is aprobabilistic logicwhich applies the ideas of aMarkov networktofirst-order logic, defining probability distributions onpossible worldson any givendomain.
In 2002,Ben Taskar,Pieter AbbeelandDaphne Kollerintroduced relational Markov networks as templates to specifyMarkov networksabstractly... | https://en.wikipedia.org/wiki/Markov_logic_network |
Inprobability theory, aMarkov modelis astochastic modelused tomodelpseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes theMarkov property). Generally, this assumption enables reasoning and computation with th... | https://en.wikipedia.org/wiki/Markov_model |
Markovian discriminationis a class ofspamfiltering methods used inCRM114and other spam filters to filter based on statistical patterns oftransition probabilitiesbetweenwordsor otherlexical tokensin spam messages that would not be captured using simplebag-of-wordsnaive Bayes spam filtering.[1]
A bag-of-words model cont... | https://en.wikipedia.org/wiki/Markovian_discrimination |
Instatistics, amaximum-entropy Markov model(MEMM), orconditional Markov model(CMM), is agraphical modelforsequence labelingthat combines features ofhidden Markov models(HMMs) andmaximum entropy(MaxEnt) models. An MEMM is adiscriminative modelthat extends a standardmaximum entropy classifierby assuming that the unknown ... | https://en.wikipedia.org/wiki/Maximum-entropy_Markov_model |
Multimodal learningis a type ofdeep learningthat integrates and processes multiple types of data, referred to asmodalities, such as text, audio, images, or video. This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal r... | https://en.wikipedia.org/wiki/Multimodal_learning |
Inmachine learning,multiple-instance learning(MIL) is a type ofsupervised learning. Instead of receiving a set of instances which are individuallylabeled, the learner receives a set of labeledbags, each containing many instances. In the simple case of multiple-instancebinary classification, a bag may be labeled negati... | https://en.wikipedia.org/wiki/Multiple_instance_learning |
Inmachine learning,multiple-instance learning(MIL) is a type ofsupervised learning. Instead of receiving a set of instances which are individuallylabeled, the learner receives a set of labeledbags, each containing many instances. In the simple case of multiple-instancebinary classification, a bag may be labeled negati... | https://en.wikipedia.org/wiki/Multiple-instance_learning |
Parity learningis a problem inmachine learning. An algorithm that solves this problem must find a functionƒ, given some samples (x,ƒ(x)) and the assurance thatƒcomputes theparityof bits at some fixed locations. The samples are generated using some distribution over the input. The problem is easy to solve usingGaussian ... | https://en.wikipedia.org/wiki/Parity_learning |
Incomputer scienceandmachine learning,population-based incremental learning(PBIL) is anoptimizationalgorithm, and anestimation of distribution algorithm. This is a type ofgenetic algorithmwhere thegenotypeof an entire population (probabilityvector) is evolved rather than individual members.[1]The algorithm is proposed ... | https://en.wikipedia.org/wiki/Population-based_incremental_learning |
Predictive learningis amachine learning(ML) technique where anartificial intelligencemodel is fed new data to develop an understanding of its environment, capabilities, and limitations. This technique finds application in many areas, includingneuroscience,business,robotics, andcomputer vision. This concept was develope... | https://en.wikipedia.org/wiki/Predictive_learning |
Preference learningis a subfield ofmachine learningthat focuses on modeling and predicting preferences based on observed preference information.[1]Preference learning typically involvessupervised learningusing datasets of pairwise preference comparisons, rankings, or other preference information.
The main task in pref... | https://en.wikipedia.org/wiki/Preference_learning |
Proactive learning[1]is a generalization ofactive learningdesigned to relax unrealistic assumptions and thereby reach practical applications.
"In real life, it is possible and more general to have multiple sources of information with differing reliabilities or areas of expertise. Active learning also assumes that the ... | https://en.wikipedia.org/wiki/Proactive_learning |
Inmachine learning,semantic analysisof atext corpusis the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents.
Semantic analysis strategies include:
Thiscomputer sciencearticle is astub. You can help Wikipedia... | https://en.wikipedia.org/wiki/Semantic_analysis_(machine_learning) |
Statistical learning theoryis a framework formachine learningdrawing from the fields ofstatisticsandfunctional analysis.[1][2][3]Statistical learning theory deals with thestatistical inferenceproblem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields... | https://en.wikipedia.org/wiki/Statistical_learning_theory |
Tanagrais a free suite ofmachine learningsoftware for research and academic purposes
developed byRicco Rakotomalalaat theLumière University Lyon 2, France.[1][2]Tanagra supports several standarddata miningtasks such as: Visualization, Descriptive statistics, Instance selection,feature selection, feature construction,re... | https://en.wikipedia.org/wiki/Tanagra_(machine_learning) |
Version space learningis alogicalapproach tomachine learning, specificallybinary classification. Version space learning algorithms search a predefined space ofhypotheses, viewed as a set oflogical sentences. Formally, the hypothesis space is adisjunction[1]
(i.e., one or more of hypotheses 1 throughnare true). A versi... | https://en.wikipedia.org/wiki/Version_space_learning |
Wafflesis a collection of command-line tools for performingmachine learningoperations developed atBrigham Young University. These tools are written inC++, and are available under theGNU Lesser General Public License.
The Waffles machine learning toolkit[1]contains command-line tools for performing various operations r... | https://en.wikipedia.org/wiki/Waffles_(machine_learning) |
Waikato Environment for Knowledge Analysis(Weka) is a collection of machine learning and data analysisfree softwarelicensed under theGNU General Public License. It was developed at theUniversity of Waikato,New Zealandand is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques... | https://en.wikipedia.org/wiki/Weka_(machine_learning) |
The Taguchi loss functionis graphical depiction oflossdeveloped by the Japanese business statisticianGenichi Taguchito describe a phenomenon affecting the value of products produced by a company. Praised by Dr.W. Edwards Deming(the business guru of the 1980s Americanqualitymovement),[1]it made clear the concept that ... | https://en.wikipedia.org/wiki/Taguchi_loss_function |
Low-energy adaptive clustering hierarchy ("LEACH")[1]is aTDMA-basedMACprotocol which is integrated with clustering and a simple routing protocol inwireless sensor networks(WSNs). The goal of LEACH is to lower the energy consumption required to create and maintain clusters in order to improve the life time of a wireless... | https://en.wikipedia.org/wiki/Low-energy_adaptive_clustering_hierarchy |
Aneural processing unit(NPU), also known asAI acceleratorordeep learning processor,is a class of specializedhardware accelerator[1]or computer system[2][3]designed to accelerateartificial intelligence(AI) andmachine learningapplications, includingartificial neural networksandcomputer vision. Their purpose is either to ... | https://en.wikipedia.org/wiki/AI_accelerator |
Aphysical neural networkis a type ofartificial neural networkin which an electrically adjustable material is used to emulate the function of aneural synapseor a higher-order (dendritic) neuron model.[1]"Physical" neural network is used to emphasize the reliance on physical hardware used to emulateneuronsas opposed to s... | https://en.wikipedia.org/wiki/Physical_neural_network |
Data miningis the process of extracting and finding patterns in massivedata setsinvolving methods at the intersection ofmachine learning,statistics, anddatabase systems.[1]Data mining is aninterdisciplinarysubfield ofcomputer scienceandstatisticswith an overall goal of extracting information (with intelligent methods) ... | https://en.wikipedia.org/wiki/Data_Mining |
Associationismis the idea thatmental processesoperate by theassociationof one mental state with its successor states.[1]It holds that all mental processes are made up of discrete psychological elements and their combinations, which are believed to be made up of sensations or simple feelings.[2]In philosophy, this idea ... | https://en.wikipedia.org/wiki/Associationism |
Behaviorismis a systematic approach to understand the behavior of humans and other animals.[1][2]It assumes that behavior is either areflexelicited by the pairing of certainantecedent stimuliin the environment, or a consequence of that individual's history, including especiallyreinforcementandpunishmentcontingencies, t... | https://en.wikipedia.org/wiki/Behaviorism |
Inmathematical logic,algebraic logicis the reasoning obtained by manipulating equations withfree variables.
What is now usually called classical algebraic logic focuses on the identification and algebraic description ofmodelsappropriate for the study of various logics (in the form of classes of algebras that constitut... | https://en.wikipedia.org/wiki/Calculus_of_relations |
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