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Maximal entropy random walk(MERW) is a popular type ofbiased random walk on a graph, in which transition probabilities are chosen accordingly to theprinciple of maximum entropy, which says that theprobability distributionwhich best represents the current state of knowledge is the one with largest entropy. While standar... | https://en.wikipedia.org/wiki/Maximal_entropy_random_walk |
Inmathematics, aself-avoiding walk(SAW) is asequenceof moves on alattice(alattice path) that does not visit the same point more than once. This is a special case of thegraph theoreticalnotion of apath. Aself-avoiding polygon(SAP) is a closed self-avoiding walk on a lattice. Very little is known rigorously about the sel... | https://en.wikipedia.org/wiki/Self-avoiding_walk |
Inprobability theoryandstatistics, aunit rootis a feature of somestochastic processes(such asrandom walks) that can cause problems instatistical inferenceinvolvingtime seriesmodels. A linearstochastic processhas a unit root if 1 is a root of the process'scharacteristic equation. Such a process isnon-stationarybut does ... | https://en.wikipedia.org/wiki/Unit_root#Unit_root_hypothesis |
Cluster(s)may refer to: | https://en.wikipedia.org/wiki/Cluster_(disambiguation) |
Instatisticsandmachine learning, thehierarchical Dirichlet process(HDP) is anonparametricBayesianapproach to clusteringgrouped data.[1][2]It uses aDirichlet processfor each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process. This method ... | https://en.wikipedia.org/wiki/Hierarchical_Dirichlet_process |
Unsupervised learningis a framework inmachine learningwhere, in contrast tosupervised learning, algorithms learn patterns exclusively from unlabeled data.[1]Other frameworks in the spectrum of supervisions includeweak- or semi-supervision, where a small portion of the data is tagged, andself-supervision. Some researche... | https://en.wikipedia.org/wiki/Unsupervised_learning |
MALLETis aJava"Machine Learning for Language Toolkit".
MALLET is an integrated collection of Java code useful for statisticalnatural language processing,document classification,cluster analysis,information extraction,topic modelingand othermachine learningapplications to text.
MALLET was developed primarily byAndrew ... | https://en.wikipedia.org/wiki/Mallet_(software_project) |
Gensimis anopen-sourcelibrary for unsupervisedtopic modeling,document indexing, retrieval by similarity, and othernatural language processingfunctionalities, using modern statisticalmachine learning.
Gensim is implemented inPythonandCythonfor performance. Gensim is designed to handle large text collections using data ... | https://en.wikipedia.org/wiki/Gensim |
Innatural language processing, asentence embeddingis a representation of a sentence as avectorof numbers which encodes meaningful semantic information.[1][2][3][4][5][6][7]
State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models.BERTpioneered an approac... | https://en.wikipedia.org/wiki/Sentence_embedding |
Data extractionis the act or process of retrievingdataout of (usuallyunstructuredor poorly structured) data sources for furtherdata processingordata storage(data migration). Theimportinto the intermediate extracting system is thus usually followed bydata transformationand possibly the addition ofmetadataprior toexportt... | https://en.wikipedia.org/wiki/Data_extraction |
Keyword extractionis tasked with the automatic identification of terms that best describe the subject of a document.[1][2]
Key phrases,key terms,key segmentsor justkeywordsare the terminology which is used for defining the terms that represent the most relevant information contained in the document. Although the termi... | https://en.wikipedia.org/wiki/Keyword_extraction |
Ontology learning(ontology extraction,ontology augmentation generation,ontology generation, orontology acquisition) is the automatic or semi-automatic creation ofontologies, including extracting the correspondingdomain'sterms and the relationships between theconceptsthat these terms represent from acorpusof natural lan... | https://en.wikipedia.org/wiki/Ontology_extraction |
In natural language processing,open information extraction(OIE) is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-arypropositions.
A proposition can be understood astruth-bearer, a textual expression of a potentialfact(e.g., "Dante w... | https://en.wikipedia.org/wiki/Open_information_extraction |
Table extractionis the process of recognizing and separating atablefrom a large document, possibly also recognizing individual rows, columns or elements.
It may be regarded as a special form ofinformation extraction.
Table extractions fromwebpagescan take advantage of the specialHTML elementsthat exist for tables, e.g... | https://en.wikipedia.org/wiki/Table_extraction |
Inmachine learningtherandom subspace method,[1]also calledattribute bagging[2]orfeature bagging, is anensemble learningmethod that attempts to reduce thecorrelationbetweenestimatorsin an ensemble by training them on random samples offeaturesinstead of the entire feature set.
In ensemble learning one tries to combine t... | https://en.wikipedia.org/wiki/Random_subspace_method |
Resampled efficient frontieris a technique ininvestment portfolioconstruction undermodern portfolio theoryto use a set of portfolios and then average them to create an effective portfolio. This will not necessarily be the optimal portfolio, but a portfolio that is more balanced between risk and the rate of return. It i... | https://en.wikipedia.org/wiki/Resampled_efficient_frontier |
Inmathematicsandcomputer science, acanonical,normal, orstandardformof amathematical objectis a standard way of presenting that object as amathematical expression. Often, it is one which provides the simplest representation of an object and allows it to be identified in a unique way. The distinction between "canonical" ... | https://en.wikipedia.org/wiki/Canonical_form |
Digital signal processing(DSP) is the use ofdigital processing, such as by computers or more specializeddigital signal processors, to perform a wide variety ofsignal processingoperations. Thedigital signalsprocessed in this manner are a sequence of numbers that representsamplesof acontinuous variablein a domain such a... | https://en.wikipedia.org/wiki/Digital_signal_processing |
Inlinear algebra,eigendecompositionis thefactorizationof amatrixinto acanonical form, whereby the matrix is represented in terms of itseigenvalues and eigenvectors. Onlydiagonalizable matricescan be factorized in this way. When the matrix being factorized is anormalor realsymmetric matrix, the decomposition is called "... | https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix |
Instatisticsandsignal processing, the method ofempirical orthogonal function(EOF) analysis is a decomposition of asignalor data set in terms oforthogonalbasis functionswhich are determined from the data. The term is also interchangeable with the geographically weightedPrincipal components analysisingeophysics.[1]
The... | https://en.wikipedia.org/wiki/Empirical_orthogonal_functions |
Inmathematics,Fourier analysis(/ˈfʊrieɪ,-iər/)[1]is the study of the way generalfunctionsmay be represented or approximated by sums of simplertrigonometric functions. Fourier analysis grew from the study ofFourier series, and is named afterJoseph Fourier, who showed that representing a function as asumof trigonometric ... | https://en.wikipedia.org/wiki/Fourier_analysis |
Inlinear algebra, thegeneralized singular value decomposition(GSVD) is the name of two different techniques based on thesingular value decomposition (SVD). The two versions differ because one version decomposes two matrices (somewhat like thehigher-order or tensor SVD) and the other version uses a set of constraints im... | https://en.wikipedia.org/wiki/Generalized_singular_value_decomposition |
Inmathematics, in particularfunctional analysis, thesingular valuesof acompact operatorT:X→Y{\displaystyle T:X\rightarrow Y}acting betweenHilbert spacesX{\displaystyle X}andY{\displaystyle Y}, are the square roots of the (necessarily non-negative)eigenvaluesof the self-adjoint operatorT∗T{\displaystyle T^{*}T}(whereT∗{... | https://en.wikipedia.org/wiki/Singular_value#Inequalities_about_singular_values |
Inapplied mathematics,k-SVDis adictionary learningalgorithm for creating a dictionary forsparse representations, via asingular value decompositionapproach.k-SVD is a generalization of thek-means clusteringmethod, and it works by iteratively alternating between sparse coding the input data based on the current dictionar... | https://en.wikipedia.org/wiki/K-SVD |
This is a list oflinear transformationsoffunctionsrelated toFourier analysis. Such transformationsmapa function to a set ofcoefficientsofbasis functions, where the basis functions aresinusoidaland are therefore strongly localized in thefrequency spectrum. (These transforms are generally designed to be invertible.) I... | https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms |
Incomputer science,locality-sensitive hashing(LSH) is afuzzy hashingtechnique that hashes similar input items into the same "buckets" with high probability.[1](The number of buckets is much smaller than the universe of possible input items.)[1]Since similar items end up in the same buckets, this technique can be used f... | https://en.wikipedia.org/wiki/Locality-sensitive_hashing |
Principal component analysis(PCA) is alineardimensionality reductiontechnique with applications inexploratory data analysis, visualization anddata preprocessing.
The data islinearly transformedonto a newcoordinate systemsuch that the directions (principal components) capturing the largest variation in the data can be ... | https://en.wikipedia.org/wiki/Non-linear_iterative_partial_least_squares |
Inmathematics, thepolar decompositionof a squarerealorcomplexmatrixA{\displaystyle A}is afactorizationof the formA=UP{\displaystyle A=UP}, whereU{\displaystyle U}is aunitary matrix, andP{\displaystyle P}is apositive semi-definiteHermitian matrix(U{\displaystyle U}is anorthogonal matrix, andP{\displaystyle P}is a positi... | https://en.wikipedia.org/wiki/Polar_decomposition |
Inlinear algebra, theSchmidt decomposition(named after its originatorErhard Schmidt) refers to a particular way of expressing avectorin thetensor productof twoinner product spaces. It has numerous applications inquantum information theory, for example inentanglementcharacterization and instate purification, andplastici... | https://en.wikipedia.org/wiki/Schmidt_decomposition |
Inmathematics, theSmith normal form(sometimes abbreviatedSNF[1]) is anormal formthat can be defined for anymatrix(not necessarilysquare) with entries in aprincipal ideal domain(PID). The Smith normal form of a matrix isdiagonal, and can be obtained from the original matrix by multiplying on the left and right byinvert... | https://en.wikipedia.org/wiki/Smith_normal_form |
Inmathematics, in particularfunctional analysis, thesingular valuesof acompact operatorT:X→Y{\displaystyle T:X\rightarrow Y}acting betweenHilbert spacesX{\displaystyle X}andY{\displaystyle Y}, are the square roots of the (necessarily non-negative)eigenvaluesof the self-adjoint operatorT∗T{\displaystyle T^{*}T}(whereT∗{... | https://en.wikipedia.org/wiki/Singular_value |
Inlinear algebra,two-dimensional singular-value decomposition(2DSVD) computes thelow-rank approximationof a set ofmatricessuch as2Dimages or weather maps in a manner almost identical to SVD (singular-value decomposition) which computes the low-rank approximation of a single matrix (or a set of 1D vectors).
Let matrixX... | https://en.wikipedia.org/wiki/Two-dimensional_singular-value_decomposition |
Inmathematics, there are many kinds ofinequalitiesinvolvingmatricesandlinear operatorsonHilbert spaces. This article covers some important operator inequalities connected withtracesof matrices.[1][2][3][4]
LetHn{\displaystyle \mathbf {H} _{n}}denote the space ofHermitiann×n{\displaystyle n\times n}matrices,Hn+{\displa... | https://en.wikipedia.org/wiki/Von_Neumann%27s_trace_inequality |
Inmathematics, awavelet seriesis a representation of asquare-integrable(real- orcomplex-valued)functionby a certainorthonormalseriesgenerated by awavelet. This article provides a formal, mathematical definition of anorthonormal waveletand of theintegral wavelet transform.[1][2][3][4]
A functionψ∈L2(R){\displaystyle \p... | https://en.wikipedia.org/wiki/Wavelet_compression |
Seq2seqis a family ofmachine learningapproaches used fornatural language processing.[1]Applications includelanguage translation,[2]image captioning,[3]conversational models,[4]speech recognition,[5]andtext summarization.[6]Seq2seq usessequence transformation: it turns one sequence into another sequence.
One naturally ... | https://en.wikipedia.org/wiki/Seq2seq |
Perceiveris a variant of theTransformerarchitecture, adapted for processing arbitrary forms of data, such as images, sounds and video, andspatial data. Unlike previous notable Transformer systems such asBERTandGPT-3, which were designed for text processing, the Perceiver is designed as a general architecture that can l... | https://en.wikipedia.org/wiki/Perceiver |
Avision transformer(ViT) is atransformerdesigned forcomputer vision.[1]A ViT decomposes an input image into a series of patches (rather than text intotokens), serializes each patch into a vector, and maps it to a smaller dimension with a singlematrix multiplication. These vectorembeddingsare then processed by atransfor... | https://en.wikipedia.org/wiki/Vision_transformer |
Alarge language model(LLM) is a type ofmachine learningmodeldesigned fornatural language processingtasks such as languagegeneration. LLMs arelanguage modelswith many parameters, and are trained withself-supervised learningon a vast amount of text.
The largest and most capable LLMs aregenerative pretrained transformers... | https://en.wikipedia.org/wiki/Large_language_model |
Bidirectional encoder representations from transformers(BERT) is alanguage modelintroduced in October 2018 by researchers atGoogle.[1][2]It learns to represent text as a sequence of vectors usingself-supervised learning. It uses theencoder-only transformerarchitecture. BERT dramatically improved thestate-of-the-artforl... | https://en.wikipedia.org/wiki/BERT_(language_model) |
T5 (Text-to-Text Transfer Transformer)is a series oflarge language modelsdeveloped byGoogle AIintroduced in 2019.[1][2]Like theoriginal Transformermodel,[3]T5 models areencoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.
T5 models are usually pretrained ... | https://en.wikipedia.org/wiki/T5_(language_model) |
Recurrent neural networks(RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, andtime series,[1]where the order of elements is important. Unlikefeedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the output... | https://en.wikipedia.org/wiki/Recurrent_neural_network |
Thetransformeris adeep learningarchitecture that was developed by researchers atGoogleand is based on the multi-headattentionmechanism, which was proposed in the 2017 paper "Attention Is All You Need".[1]Text is converted to numerical representations calledtokens, and each token is converted into a vector via lookup fr... | https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture) |
Attentionorfocus, is the concentration ofawarenesson somephenomenonto the exclusion of other stimuli.[1]It is the selective concentration on discrete information, eithersubjectivelyorobjectively.William James(1890) wrote that "Attention is the taking possession by the mind, in clear and vivid form, of one out of what s... | https://en.wikipedia.org/wiki/Attention |
There are manytypes of artificial neural networks(ANN).
Artificial neural networksarecomputational modelsinspired bybiological neural networks, and are used toapproximatefunctionsthat are generally unknown. Particularly, they are inspired by the behaviour ofneuronsand the electrical signals they convey between input (... | https://en.wikipedia.org/wiki/Dynamic_neural_network |
In mathematics, for example in the study of statistical properties ofgraphs, anull modelis a type of random object that matches one specific object in some of its features, or more generally satisfies a collection of constraints, but which is otherwise taken to be an unbiasedly random structure. The null model is used ... | https://en.wikipedia.org/wiki/Null_model |
Instatistical physicsandmathematics,percolation theorydescribes the behavior of a network when nodes or links are added. This is a geometric type ofphase transition, since at a critical fraction of addition the network of small, disconnected clusters merge into significantly largerconnected, so-called spanning clusters... | https://en.wikipedia.org/wiki/Percolation_theory |
Actor–network theory(ANT) is a theoretical and methodological approach tosocial theorywhere everything in the social and natural worlds exists in constantly shifting networks of relationships. It posits that nothing exists outside those relationships. All the factors involved in a social situation are on the same level... | https://en.wikipedia.org/wiki/Actor-network_theory |
Blockmodelingis a set or a coherentframework, that is used for analyzingsocial structureand also for setting procedure(s) for partitioning (clustering)social network's units (nodes,vertices,actors), based on specific patterns, which form a distinctive structure through interconnectivity.[1][2]It is primarily used insta... | https://en.wikipedia.org/wiki/Blockmodeling |
Digital humanities(DH) is an area of scholarly activity at the intersection ofcomputingordigital technologiesand the disciplines of thehumanities. It includes the systematic use of digital resources in the humanities, as well as the analysis of their application.[1][2]DH can be defined as new ways of doing scholarship ... | https://en.wikipedia.org/wiki/Digital_humanities |
Dynamic network analysis(DNA) is an emergent scientific field that brings together traditionalsocial network analysis(SNA),link analysis(LA),social simulationandmulti-agent systems(MAS) withinnetwork scienceandnetwork theory. Dynamic networks are afunctionoftime(modeled as asubsetof thereal numbers) to a set ofgraphs; ... | https://en.wikipedia.org/wiki/Dynamic_network_analysis |
Thefriendship paradoxis the phenomenon first observed by the sociologistScott L. Feldin 1991 that on average, an individual's friends have more friends than that individual.[1]It can be explained as a form ofsampling biasin which people with more friends are more likely to be in one's own friend group. In other words, ... | https://en.wikipedia.org/wiki/Friendship_paradox |
Individualhuman mobilityis the study that describes how individual humans move within a network or system.[1]The concept has been studied in a number of fields originating in the study of demographics. Understanding human mobility has many applications in diverse areas, includingspread of diseases,[2][3]mobile viruses... | https://en.wikipedia.org/wiki/Individual_mobility |
Influence-for-hireorcollective influence, refers to the economy that has emerged around buying and sellinginfluenceonsocial media platforms.[1]
Companies that engage in the influence-for-hire industry range fromcontent farmsto high-endpublic relationsagencies. Traditionallyinfluence operationshave largely been confine... | https://en.wikipedia.org/wiki/Influence-for-hire |
1800s:Martineau·Tocqueville·Marx·Spencer·Le Bon·Ward·Pareto·Tönnies·Veblen·Simmel·Durkheim·Addams·Mead·Weber·Du Bois·Mannheim·Elias
Mathematical sociologyis aninterdisciplinaryfield of research concerned with the use of mathematics within sociological research.[1]
Starting in the early 1940s,Nicolas Rashevsky,[2][3]a... | https://en.wikipedia.org/wiki/Mathematical_sociology |
Metcalfe's lawstates that the financial value or influence of atelecommunications networkisproportional to the squareof the number of connected users of the system (n2). The law is named afterRobert Metcalfeand was first proposed in 1980, albeit not in terms of users, but rather of "compatible communicating devices" (e... | https://en.wikipedia.org/wiki/Metcalfe%27s_law |
Netocracywas a term invented by the editorial board of the American technology magazineWiredin the early 1990s. AportmanteauofInternetandaristocracy,netocracyrefers to a perceived global upper-class that bases its power on a technological advantage and networking skills, in comparison to what is portrayed as abourgeois... | https://en.wikipedia.org/wiki/Netocracy |
Network-based diffusion analysis (NBDA)is a statistical tool to detect and quantify social transmission of information or a behaviour in social networks (SNA, etc.). NBDA assumes thatsocial transmissionof a behavior follows the social network of associations or interactions among individuals, since individuals who spen... | https://en.wikipedia.org/wiki/Network-based_diffusion_analysis |
Organizational patternsare inspired in large part by the principles of the software pattern community, that in turn takes it cues fromChristopher Alexander's work on patterns of the built world.[1]Organizational patterns also have roots inKroeber's classic anthropological texts on the patterns that underlie culture and... | https://en.wikipedia.org/wiki/Organizational_patterns |
Thesmall-world experimentcomprised several experiments conducted byStanley Milgramand other researchers examining theaverage path lengthforsocial networksof people in the United States.[1]The research was groundbreaking in that it suggested that human society is asmall-world-type network characterized by short path-len... | https://en.wikipedia.org/wiki/Small_world_phenomenon |
Social media analyticsorsocial media monitoringis the process of gathering and analyzing data fromsocial networkssuch asFacebook,Instagram,LinkedIn, orTwitter. A part of social media analytics is calledsocial media monitoringorsocial listening. It is commonly used by marketers to track online conversations about produc... | https://en.wikipedia.org/wiki/Social_media_analytics |
Social media intelligence(SMIorSOCMINT) comprises the collective tools and solutions that allow organizations to analyze conversations, respond to synchronize social signals, and synthesize socialdata pointsinto meaningful trends and analysis, based on the user's needs. Social media intelligence allows one to utilizein... | https://en.wikipedia.org/wiki/Social_media_intelligence |
Social media miningis the process of obtaining data fromuser-generated contenton social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process ofminingfor minera... | https://en.wikipedia.org/wiki/Social_media_mining |
1800s:Martineau·Tocqueville·Marx·Spencer·Le Bon·Ward·Pareto·Tönnies·Veblen·Simmel·Durkheim·Addams·Mead·Weber·Du Bois·Mannheim·Elias
Asocial networkis asocial structureconsisting of a set ofsocialactors (such asindividualsor organizations), networks ofdyadicties, and othersocial interactionsbetween actors. The social n... | https://en.wikipedia.org/wiki/Social_network |
Social network analysis(SNA)softwareissoftwarewhich facilitatesquantitativeorqualitativeanalysis of social networks, by describing features of a network either through numerical orvisual representation.
Networks can consist of anything from families,[1]project teams,classrooms,sports teams,legislatures,nation-states,d... | https://en.wikipedia.org/wiki/Social_network_analysis_software |
Asocial networking service(SNS), orsocial networking site, is a type of onlinesocial mediaplatform which people use to buildsocial networksorsocial relationshipswith other people who share similar personal or career content, interests, activities, backgrounds or real-life connections.[1][2]
Social networking services ... | https://en.wikipedia.org/wiki/Social_networking_service |
Social software, also known associal appsorsocial platformincludes communications and interactive tools that are often based on theInternet. Communication tools typically handle capturing, storing and presenting communication, usually written but increasingly including audio and video as well. Interactive tools handle ... | https://en.wikipedia.org/wiki/Social_software |
Thesocial webis a set ofsocial relationsthat link people through theWorld Wide Web.[1]The social web encompasses howwebsitesandsoftwarearedesignedanddevelopedin order to support and fostersocial interaction.[2]: 5These online social interactions form the basis of much online activity includingonline shopping,[3]educati... | https://en.wikipedia.org/wiki/Social_web |
Sociomappingis a method developed forprocessingandvisualizationof relational data (e.g. social network data). It is most commonly used for mapping the social structure within small teams (10-25 people). Sociomapping uses the landscape metaphor to display complex multi-dimensional data in a 3Dmap, where individual objec... | https://en.wikipedia.org/wiki/Sociomapping |
Hauntology(aportmanteauofhauntingandontology, alsospectral studies,spectralities, or thespectral turn) is a range of ideas referring to the return or persistence of elements from the social or cultural past, as in the manner of a ghost. The term is aneologismfirst introduced by French philosopherJacques Derridain his 1... | https://en.wikipedia.org/wiki/Hauntology |
Pruningis adata compressiontechnique inmachine learningandsearch algorithmsthat reduces the size ofdecision treesby removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the finalclassifier, and hence improves predictive accuracy by the reduction ofov... | https://en.wikipedia.org/wiki/Decision-tree_pruning |
Incomputer science, abinary decision diagram(BDD) orbranching programis adata structurethat is used to represent aBoolean function. On a more abstract level, BDDs can be considered as acompressedrepresentation ofsetsorrelations. Unlike other compressed representations, operations are performed directly on the compresse... | https://en.wikipedia.org/wiki/Binary_decision_diagram |
Chi-square automatic interaction detection(CHAID)[1]is adecision treetechnique based on adjusted significance testing (Bonferroni correction,Holm-Bonferroni testing).[2][3]
CHAID is based on a formal extension of AID (Automatic Interaction Detection)[4]and THAID (THeta Automatic Interaction Detection)[5][6]procedures ... | https://en.wikipedia.org/wiki/CHAID |
Predictive analytics, orpredictive AI, encompasses a variety ofstatisticaltechniques fromdata mining,predictive modeling, andmachine learningthat analyze current and historical facts to makepredictionsabout future or otherwise unknown events.[1]
In business, predictive models exploitpatternsfound in historical and tra... | https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_(CART) |
Indecision tree learning,ID3(Iterative Dichotomiser 3) is analgorithminvented byRoss Quinlan[1]used to generate adecision treefrom a dataset. ID3 is the precursor to theC4.5 algorithm, and is typically used in themachine learningandnatural language processingdomains.
The ID3 algorithm begins with the original setS{\di... | https://en.wikipedia.org/wiki/ID3_algorithm |
C4.5is an algorithm used to generate adecision treedeveloped byRoss Quinlan.[1]C4.5 is an extension of Quinlan's earlierID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as astatistical classifier. In 2011, authors of theWekamachine learni... | https://en.wikipedia.org/wiki/C4.5_algorithm |
Adecision stumpis amachine learning modelconsisting of a one-leveldecision tree.[1]That is, it is a decision tree with one internal node (the root) which is immediately connected to the terminal nodes (its leaves). A decision stump makes a prediction based on the value of just a single input feature. Sometimes they are... | https://en.wikipedia.org/wiki/Decision_stump |
AdaBoost(short forAdaptiveBoosting) is astatistical classificationmeta-algorithmformulated byYoav FreundandRobert Schapirein 1995, who won the 2003Gödel Prizefor their work. It can be used in conjunction with many types of learning algorithm to improve performance. The output of multipleweak learnersis combined into a ... | https://en.wikipedia.org/wiki/AdaBoost |
Anincremental decision treealgorithm is anonlinemachine learningalgorithm that outputs adecision tree. Manydecision treemethods, such asC4.5, construct a tree using a complete dataset. Incremental decision tree methods allow an existing tree to be updated using only new individual data instances, without having to re-p... | https://en.wikipedia.org/wiki/Incremental_decision_tree |
Analternating decision tree(ADTree) is amachine learningmethod for classification. It generalizesdecision treesand has connections toboosting.
An ADTree consists of an alternation of decision nodes, which specify a predicate condition, and prediction nodes, which contain a single number. An instance is classified by ... | https://en.wikipedia.org/wiki/Alternating_decision_tree |
Structured data analysisis thestatistical data analysisof structured data. This can arise either in the form of ana prioristructure such as multiple-choice questionnaires or in situations with the need to search forstructurethat fits the given data, either exactly or approximately. This structure can then be used for m... | https://en.wikipedia.org/wiki/Structured_data_analysis_(statistics) |
Incomputer science, alogistic model tree(LMT) is aclassificationmodel with an associatedsupervised trainingalgorithmthat combineslogistic regression(LR) anddecision tree learning.[1][2]
Logistic model trees are based on the earlier idea of a model tree: a decision tree that haslinear regressionmodels at its leaves to ... | https://en.wikipedia.org/wiki/Logistic_model_tree |
Indata miningandstatistics,hierarchical clustering(also calledhierarchical cluster analysisorHCA) is a method ofcluster analysisthat seeks to build ahierarchyof clusters. Strategies for hierarchical clustering generally fall into two categories:
In general, the merges and splits are determined in agreedymanner. The re... | https://en.wikipedia.org/wiki/Hierarchical_clustering |
Automatic clustering algorithmsare algorithms that can perform clustering without prior knowledge of data sets. In contrast with othercluster analysistechniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points.[1][needs context]
Given a set ... | https://en.wikipedia.org/wiki/Automatic_clustering_algorithms |
Balanced clusteringis a special case ofclusteringwhere, in the strictest sense, cluster sizes are constrained to⌊nk⌋{\displaystyle \lfloor {n \over k}\rfloor }or⌈nk⌉{\displaystyle \lceil {n \over k}\rceil }, wheren{\displaystyle n}is the number of points andk{\displaystyle k}is the number of clusters.[1]A typical algor... | https://en.wikipedia.org/wiki/Balanced_clustering |
Conceptual clusteringis amachine learningparadigm forunsupervised classificationthat has been defined byRyszard S. Michalskiin 1980 (Fisher 1987, Michalski 1980) and developed mainly during the 1980s. It is distinguished from ordinarydata clusteringby generating aconcept descriptionfor each generated class. Most conce... | https://en.wikipedia.org/wiki/Conceptual_clustering |
Consensus clusteringis a method of aggregating (potentially conflicting) results from multipleclustering algorithms. Also calledcluster ensembles[1]or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and ... | https://en.wikipedia.org/wiki/Consensus_clustering |
Incomputer science,constrained clusteringis a class ofsemi-supervised learningalgorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with adata clusteringalgorithm.[1]A cluster in which the members conform to all must-link and cannot-link cons... | https://en.wikipedia.org/wiki/Constrained_clustering |
In the study ofcomplex networks, a network is said to havecommunity structureif the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the particular case ofnon-overlappingcommunity finding, this implies that the networ... | https://en.wikipedia.org/wiki/Community_structure#Algorithms_for_finding_communities |
Incomputer science,data stream clusteringis defined as theclusteringof data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as astreaming algorithmand the objective is, given a sequence of points, to construct a good clustering o... | https://en.wikipedia.org/wiki/Data_stream_clustering |
TheHCS (Highly Connected Subgraphs) clustering algorithm[1](also known as theHCS algorithm, and other names such asHighly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity forcluster analysis. It works by representing the similarity data in a similarity graph, and then finding all the h... | https://en.wikipedia.org/wiki/HCS_clustering_algorithm |
Inbioinformatics,sequence clusteringalgorithmsattempt to groupbiological sequencesthat are somehow related. The sequences can be either ofgenomic, "transcriptomic" (ESTs) orproteinorigin.
For proteins,homologous sequencesare typically grouped intofamilies. For EST data, clustering is important to group sequences orig... | https://en.wikipedia.org/wiki/Sequence_clustering |
Inmultivariate statistics,spectral clusteringtechniques make use of thespectrum(eigenvalues) of thesimilarity matrixof the data to performdimensionality reductionbeforeclusteringin fewer dimensions. The similarity matrix is provided as an input and consists of a quantitative assessment of the relative similarity of eac... | https://en.wikipedia.org/wiki/Spectral_clustering |
Ahierarchy(fromGreek:ἱεραρχία,hierarkhia, 'rule of a high priest', fromhierarkhes, 'president of sacred rites') is an arrangement of items (objects, names, values, categories, etc.) that are represented as being "above", "below", or "at the same level as" one another. Hierarchy is an important concept in a wide variety... | https://en.wikipedia.org/wiki/Hierarchy |
Inmathematics,computer scienceandnetwork science,network theoryis a part ofgraph theory. It definesnetworksasgraphswhere the vertices or edges possess attributes. Network theory analyses these networks over thesymmetric relationsorasymmetric relationsbetween their (discrete) components.
Network theory has applications... | https://en.wikipedia.org/wiki/Network_theory |
Theclique gameis apositional gamewhere two players alternately pick edges, trying to occupy a completecliqueof a given size.
The game is parameterized by two integersn>k. The game-board is the set of all edges of acomplete graphonnvertices. The winning-sets are all the cliques onkvertices. There are several variants o... | https://en.wikipedia.org/wiki/Clique_game |
GloVe, coined from Global Vectors, is a model for distributed word representation. The model is anunsupervised learningalgorithm for obtainingvector representationsfor words. This is achieved by mapping words into a meaningful space where the distance between words is related to semantic similarity.[1]Training is perfo... | https://en.wikipedia.org/wiki/GloVe |
Closenessis a basic concept intopologyand related areas inmathematics. Intuitively, we say two sets are close if they are arbitrarily near to each other. The concept can be defined naturally in ametric spacewhere a notion of distance between elements of the space is defined, but it can be generalized totopological spac... | https://en.wikipedia.org/wiki/Closeness_(mathematics) |
Agraphoidis a set of statements of the form, "Xis irrelevant toYgiven that we knowZ" whereX,YandZare sets of variables. The notion of "irrelevance" and "given that we know" may obtain different interpretations, includingprobabilistic,relationaland correlational, depending on the application. These interpretations share... | https://en.wikipedia.org/wiki/Graphoid |
Inprobability theory,conditional dependenceis a relationship between two or moreeventsthat aredependentwhen a third event occurs.[1][2]For example, ifA{\displaystyle A}andB{\displaystyle B}are two events that individually increase the probability of a third eventC,{\displaystyle C,}and do not directly affect each other... | https://en.wikipedia.org/wiki/Conditional_dependence |
Inprobability theory,de Finetti's theoremstates thatexchangeableobservations areconditionally independentrelative to somelatent variable. Anepistemic probabilitydistributioncould then be assigned to this variable. It is named in honor ofBruno de Finetti, and one of its uses is in providing a pragmatic approach to de Fi... | https://en.wikipedia.org/wiki/De_Finetti%27s_theorem |
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