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64-515: [REDACTED] Look up recognition in Wiktionary, the free dictionary. Recognition may refer to: Machine learning [ edit ] Pattern recognition , a branch of machine learning which encompasses the meanings below Biometric [ edit ] Recognition of human individuals , or biometrics, used as a form of identification and access control Facial recognition system ,

128-428: A b e l ) {\displaystyle p({{\boldsymbol {x}}|{\rm {label}}})} is instead estimated and combined with the prior probability p ( l a b e l | θ ) {\displaystyle p({\rm {label}}|{\boldsymbol {\theta }})} using Bayes' rule , as follows: When the labels are continuously distributed (e.g., in regression analysis ),

192-409: A real-valued output to each input; sequence labeling , which assigns a class to each member of a sequence of values (for example, part of speech tagging , which assigns a part of speech to each word in an input sentence); and parsing , which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence. Pattern recognition algorithms generally aim to provide

256-505: A regularization procedure that favors simpler models over more complex models. In a Bayesian context, the regularization procedure can be viewed as placing a prior probability p ( θ ) {\displaystyle p({\boldsymbol {\theta }})} on different values of θ {\displaystyle {\boldsymbol {\theta }}} . Mathematically: where θ ∗ {\displaystyle {\boldsymbol {\theta }}^{*}}

320-465: A distinction was already made between the ' a priori ' and the ' a posteriori ' knowledge. Later Kant defined his distinction between what is a priori known – before observation – and the empirical knowledge gained from observations. In a Bayesian pattern classifier, the class probabilities p ( l a b e l | θ ) {\displaystyle p({\rm {label}}|{\boldsymbol {\theta }})} can be chosen by

384-438: A frequentist or a Bayesian approach. Within medical science, pattern recognition is the basis for computer-aided diagnosis (CAD) systems. CAD describes a procedure that supports the doctor's interpretations and findings. Other typical applications of pattern recognition techniques are automatic speech recognition , speaker identification , classification of text into several categories (e.g., spam or non-spam email messages),

448-428: A large number of samples of X {\displaystyle {\mathcal {X}}} and hand-labeling them using the correct value of Y {\displaystyle {\mathcal {Y}}} (a time-consuming process, which is typically the limiting factor in the amount of data of this sort that can be collected). The particular loss function depends on the type of label being predicted. For example, in

512-438: A larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration. It originated in engineering , and the term is popular in the context of computer vision : a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition . In machine learning , pattern recognition

576-533: A particular word in an email) or real-valued (e.g., a measurement of blood pressure). Often, categorical and ordinal data are grouped together, and this is also the case for integer-valued and real-valued data. Many algorithms work only in terms of categorical data and require that real-valued or integer-valued data be discretized into groups (e.g., less than 5, between 5 and 10, or greater than 10). Many common pattern recognition algorithms are probabilistic in nature, in that they use statistical inference to find

640-421: A population of T cells that are specialized for inducing programmed cell death of other cells. Cytotoxic T cells regularly patrol all body cells to maintain the organismal homeostasis. Whenever they encounter signs of disease, caused for example by the presence of viruses or intracellular bacteria or a transformed tumor cell, they initiate processes to destroy the potentially harmful cell. All nucleated cells in

704-411: A reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and

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768-425: A studio album by Fred V & Grafix, released in 2014 "Recognise", a song by Lost Frequencies from the 2019 album Alive and Feeling Fine "Recognize" (song) , a 2014 song by PartyNextDoor "Recognize", a song by Ol' Dirty Bastard from the 1999 album Nigga Please "Recognize", a song by DJ Snake from the 2019 album Carte Blanche Law [ edit ] Recognition (parliamentary procedure) ,

832-420: A system to identify individuals by their facial characteristics Fingerprint recognition , automated method of verifying a match between two human fingerprints Handwritten biometric recognition , identifies the author of specific handwriting, offline (static) or in real-time (dynamic) Iris recognition , a method of biometric identification Linguistic [ edit ] Language identification ,

896-438: A total of n {\displaystyle n} features the powerset consisting of all 2 n − 1 {\displaystyle 2^{n}-1} subsets of features need to be explored. The Branch-and-Bound algorithm does reduce this complexity but is intractable for medium to large values of the number of available features n {\displaystyle n} Techniques to transform

960-528: A unique sequence of a single peptide among thousands of other peptides presented on the same cell, because an MHC molecule in one cell can bind to quite a large range of peptides. Predicting which (fragments of) antigens will be presented to the immune system by a certain MHC/HLA type is difficult, but the technology involved is improving. Cytotoxic T cells (also known as T c , killer T cell, or cytotoxic T-lymphocyte (CTL)) express CD8 co-receptors and are

1024-409: Is a special case in which MHC-I molecules are able to present extracellular antigens, usually displayed only by MHC-II molecules. This ability appears in several APCs, mainly plasmacytoid dendritic cells in tissues that stimulate CD8+ T cells directly. This process is essential when APCs are not directly infected, triggering local antiviral and anti-tumor immune responses immediately without trafficking

1088-405: Is equivalent to maximizing the number of correctly classified instances). The goal of the learning procedure is then to minimize the error rate (maximize the correctness ) on a "typical" test set. For a probabilistic pattern recognizer, the problem is instead to estimate the probability of each possible output label given a particular input instance, i.e., to estimate a function of the form where

1152-533: Is fairly small (e.g., in the case of classification ), N may be set so that the probability of all possible labels is output. Probabilistic algorithms have many advantages over non-probabilistic algorithms: Feature selection algorithms attempt to directly prune out redundant or irrelevant features. A general introduction to feature selection which summarizes approaches and challenges, has been given. The complexity of feature-selection is, because of its non-monotonous character, an optimization problem where given

1216-574: Is favored by gradual reduction of the pH. The main proteases in endosomes are cathepsins and the result is the degradation of the antigens into oligopeptides. MHC-II molecules are transported from the ER to the MHC class II loading compartment together with the protein invariant chain (Ii, CD74). A non classical MHC-II molecule ( HLA-DO and HLA-DM ) catalyses the exchange of part of the CD74 ( CLIP peptide ) with

1280-436: Is generally categorized according to the type of learning procedure used to generate the output value. Supervised learning assumes that a set of training data (the training set ) has been 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 that attempts to meet two sometimes conflicting objectives: Perform as well as possible on

1344-406: Is included in the search capabilities of many text editors and word processors . A modern definition of pattern recognition is: The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. Pattern recognition

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1408-583: Is more cell-specific than MHC-I. APCs usually internalise exogenous antigens by endocytosis , but also by pinocytosis , macroautophagy , endosomal microautophagy or chaperone-mediated autophagy . In the first case, after internalisation, the antigens are enclosed in vesicles called endosomes . There are three compartments involved in this antigen presentation pathway: early endosomes, late endosomes or endolysosomes and lysosomes , where antigens are hydrolized by lysosome-associated enzymes (acid-dependent hydrolases, glycosidases, proteases, lipases). This process

1472-413: Is normally known as clustering , based on the common perception of the task as involving no training data to speak of, and of grouping the input data into clusters based on some inherent similarity measure (e.g. the distance between instances, considered as vectors in a multi-dimensional vector space ), rather than assigning each input instance into one of a set of pre-defined classes. In some fields,

1536-421: Is some representation of an email and y {\displaystyle y} is either "spam" or "non-spam"). In order for this to be a well-defined problem, "approximates as closely as possible" needs to be defined rigorously. In decision theory , this is defined by specifying a loss function or cost function that assigns a specific value to "loss" resulting from producing an incorrect label. The goal then

1600-506: Is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as generative or discriminative . Parametric: Nonparametric: Unsupervised: Antigen recognition Antigen presentation is a vital immune process that is essential for T cell immune response triggering. Because T cells recognize only fragmented antigens displayed on cell surfaces , antigen processing must occur before

1664-1085: Is that the resulting features after feature extraction has taken place are of a different sort than the original features and may not easily be interpretable, while the features left after feature selection are simply a subset of the original features. The problem of pattern recognition can be stated as follows: Given an unknown function g : X → Y {\displaystyle g:{\mathcal {X}}\rightarrow {\mathcal {Y}}} (the ground truth ) that maps input instances x ∈ X {\displaystyle {\boldsymbol {x}}\in {\mathcal {X}}} to output labels y ∈ Y {\displaystyle y\in {\mathcal {Y}}} , along with training data D = { ( x 1 , y 1 ) , … , ( x n , y n ) } {\displaystyle \mathbf {D} =\{({\boldsymbol {x}}_{1},y_{1}),\dots ,({\boldsymbol {x}}_{n},y_{n})\}} assumed to represent accurate examples of

1728-468: Is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification , which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam"). Pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression , which assigns

1792-404: Is the value used for θ {\displaystyle {\boldsymbol {\theta }}} in the subsequent evaluation procedure, and p ( θ | D ) {\displaystyle p({\boldsymbol {\theta }}|\mathbf {D} )} , the posterior probability of θ {\displaystyle {\boldsymbol {\theta }}} , is given by In

1856-508: Is to minimize the expected loss, with the expectation taken over the probability distribution of X {\displaystyle {\mathcal {X}}} . In practice, neither the distribution of X {\displaystyle {\mathcal {X}}} nor the ground truth function g : X → Y {\displaystyle g:{\mathcal {X}}\rightarrow {\mathcal {Y}}} are known exactly, but can be computed only empirically by collecting

1920-464: Is used to make sense of and identify objects, and is closely related to perception. This explains how the sensory inputs humans receive are made meaningful. Pattern recognition can be thought of in two different ways. The first concerns template matching and the second concerns feature detection. A template is a pattern used to produce items of the same proportions. The template-matching hypothesis suggests that incoming stimuli are compared with templates in

1984-523: The Bayesian approach to this problem, instead of choosing a single parameter vector θ ∗ {\displaystyle {\boldsymbol {\theta }}^{*}} , the probability of a given label for a new instance x {\displaystyle {\boldsymbol {x}}} is computed by integrating over all possible values of θ {\displaystyle {\boldsymbol {\theta }}} , weighted according to

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2048-444: The automatic recognition of handwriting on postal envelopes, automatic recognition of images of human faces, or handwriting image extraction from medical forms. The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name

2112-485: The covariance matrix . Also the probability of each class p ( l a b e l | θ ) {\displaystyle p({\rm {label}}|{\boldsymbol {\theta }})} is estimated from the collected dataset. Note that the usage of ' Bayes rule ' in a pattern classifier does not make the classification approach Bayesian. Bayesian statistics has its origin in Greek philosophy where

2176-414: The dot product or the angle between two vectors. Features typically are either categorical (also known as nominal , i.e., consisting of one of a set of unordered items, such as a gender of "male" or "female", or a blood type of "A", "B", "AB" or "O"), ordinal (consisting of one of a set of ordered items, e.g., "large", "medium" or "small"), integer-valued (e.g., a count of the number of occurrences of

2240-427: The feature vector input is x {\displaystyle {\boldsymbol {x}}} , and the function f is typically parameterized by some parameters θ {\displaystyle {\boldsymbol {\theta }}} . In a discriminative approach to the problem, f is estimated directly. In a generative approach, however, the inverse probability p ( x | l

2304-563: The proteasome , but there are also other cytoplasmic proteolytic pathways. Then, peptides are distributed to the endoplasmic reticulum (ER) via the action of heat shock proteins and the transporter associated with antigen processing (TAP) which translocates the cytosolic peptides into the ER lumen in an ATP-dependent transport mechanism. There are several ER chaperones involved in MHC-I assembly, such as calnexin , calreticulin , Erp57, protein disulfide isomerase (PDI), and tapasin . Specifically,

2368-471: The APCs in the local lymph nodes. Antigens from the extracellular space and sometimes also endogenous ones, are enclosed into endocytic vesicles and presented on the cell surface by MHC-II molecules to the helper T cells expressing CD4 molecule . Only APCs such as dendritic cells , B cells or macrophages express MHC-II molecules on their surface in substantial quantity, so expression of MHC-II molecules

2432-456: The antigen by MHC molecules. There are two types of MHC molecules which differ in the behaviour of the antigens: MHC class I molecules (MHC-I) bind peptides from the cell cytosol , while peptides generated in the endocytic vesicles after internalisation are bound to MHC class II (MHC-II). Cellular membranes separate these two cellular environments - intracellular and extracellular. Each T cell can only recognize tens to hundreds of copies of

2496-401: The antigen fragment can be recognized by a T-cell receptor . Specifically, the fragment, bound to the major histocompatibility complex (MHC) , is transported to the surface of the antigen-presenting cell , a process known as presentation. If there has been an infection with viruses or bacteria, the antigen-presenting cell will present an endogenous or exogenous peptide fragment derived from

2560-470: The assignment of the floor Recognition (tax) , an income tax concept Recognition (family law) , a process in some jurisdictions that confers legitimacy on a child Diplomatic recognition , the acceptance of the sovereignty of a political entity Legal recognition , recognition of a legal right in a jurisdiction Other uses [ edit ] Recognise, a campaign by Reconciliation Australia for constitutional reform Aircraft recognition ,

2624-410: The banking industry Optical character recognition , the conversion of typewritten or printed text into machine-encoded text Automatic number plate recognition , the use of optical character recognition to read vehicle registration plates Other meanings in computer science [ edit ] Activity recognition , the recognition of events from videos or sensors Gesture recognition ,

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2688-416: The best label for a given instance. Unlike other algorithms, which simply output a "best" label, often probabilistic algorithms also output a probability of the instance being described by the given label. In addition, many probabilistic algorithms output a list of the N -best labels with associated probabilities, for some value of N , instead of simply a single best label. When the number of possible labels

2752-502: The body (along with platelets ) display class I major histocompatibility complex (MHC-I molecules). Antigens generated endogenously within these cells are bound to MHC-I molecules and presented on the cell surface. This antigen presentation pathway enables the immune system to detect transformed or infected cells displaying peptides from modified-self (mutated) or foreign proteins. In the presentation process, these proteins are mainly degraded into small peptides by cytosolic proteases in

2816-476: The case of classification , the simple zero-one loss function is often sufficient. This corresponds simply to assigning a loss of 1 to any incorrect labeling and implies that the optimal classifier minimizes the error rate on independent test data (i.e. counting up the fraction of instances that the learned function h : X → Y {\displaystyle h:{\mathcal {X}}\rightarrow {\mathcal {Y}}} labels wrongly, which

2880-520: The complex of TAP, tapasin, MHS Class 1, ERp57, and calreticulin is called the peptide-loading complex (PLC). Peptides are loaded to MHC-I peptide binding groove between two alpha helices at the bottom of the α1 and α2 domains of the MHC class I molecule. After releasing from tapasin, peptide-MHC-I complexes (pMHC-I) exit the ER and are transported to the cell surface by exocytic vesicles. Naïve anti-viral T cells ( CD8+ ) cannot directly eliminate transformed or infected cells. They have to be activated by

2944-482: The denominator involves integration rather than summation: The value of θ {\displaystyle {\boldsymbol {\theta }}} is typically learned using maximum a posteriori (MAP) estimation. This finds the best value that simultaneously meets two conflicting objects: To perform as well as possible on the training data (smallest error-rate ) and to find the simplest possible model. Essentially, this combines maximum likelihood estimation with

3008-442: The face Pareidolia , a psychological phenomenon in which a vague stimulus is perceived as significant Recall (memory) , the retrieval of events or information from the past Emotion recognition Pattern recognition (psychology) In other sciences [ edit ] Recognition (sociology) , a public acknowledgement of person's status or merits Antigen recognition , in immunology Intra-species recognition ,

3072-435: The interpretation of human gestures Named entity recognition , the classification of elements in text into predefined categories Object recognition Optical mark recognition , the capturing of human-marked data from document forms Sound recognition Neuroscience and psychology [ edit ] Visual object recognition Face perception , the process by which the human brain understands and interprets

3136-496: The long-term memory. If there is a match, the stimulus is identified. Feature detection models, such as the Pandemonium system for classifying letters (Selfridge, 1959), suggest that the stimuli are broken down into their component parts for identification. One observation is a capital E having three horizontal lines and one vertical line. Algorithms for pattern recognition depend on the type of label output, on whether learning

3200-398: The mapping, produce a function h : X → Y {\displaystyle h:{\mathcal {X}}\rightarrow {\mathcal {Y}}} that approximates as closely as possible the correct mapping g {\displaystyle g} . (For example, if the problem is filtering spam, then x i {\displaystyle {\boldsymbol {x}}_{i}}

3264-464: The pMHC-I complexes of antigen-presenting cells (APCs). Here, antigen can be presented directly (as described above) or indirectly ( cross-presentation ) from virus-infected and non-infected cells. After the interaction between pMHC-I and TCR, in presence of co-stimulatory signals and/or cytokines, T cells are activated, migrate to the peripheral tissues and kill the target cells (infected or damaged cells) by inducing cytotoxicity . Cross-presentation

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3328-743: The peptide antigen. Peptide-MHC-II complexes (pMHC-II) are transported to the plasma membrane and the processed antigen is presented to the helper T cells in the lymph nodes. APCs undergo a process of maturation while migrating, via chemotactic signals, to lymphoid tissues, in which they lose the phagocytic capacity and develop an increased ability to communicate with T-cells by antigen-presentation. As well as in CD8+ cytotoxic T cells, APCs need pMHC-II and additional costimulatory signals to fully activate naive T helper cells. Alternative pathway of endogenous antigen processing and presentation over MHC-II molecules exists in medullary thymic epithelial cells (mTEC) via

3392-402: The posterior probability: The first pattern classifier – the linear discriminant presented by Fisher – was developed in the frequentist tradition. The frequentist approach entails that the model parameters are considered unknown, but objective. The parameters are then computed (estimated) from the collected data. For the linear discriminant, these parameters are precisely the mean vectors and

3456-509: The problem of identifying which natural language given content is in Natural language understanding , the parsing of the meaning of text Speech recognition , the conversion of spoken words into text Speaker recognition , the recognition of a speaker from their voice Textual [ edit ] Handwriting recognition , the conversion of handwritten text into machine-encoded text Magnetic ink character recognition , used mainly by

3520-429: The process of autophagy . It is important for the process of central tolerance of T cells in particular the negative selection of autoreactive clones. Random gene expression of the whole genome is achieved via the action of AIRE and a self-digestion of the expressed molecules presented on both MHC-I and MHC-II molecules. B-cell receptors on the surface of B cells bind to intact native and undigested antigens of

3584-448: The raw feature vectors ( feature extraction ) are sometimes used prior to application of the pattern-matching algorithm. Feature extraction algorithms attempt to reduce a large-dimensionality feature vector into a smaller-dimensionality vector that is easier to work with and encodes less redundancy, using mathematical techniques such as principal components analysis (PCA). The distinction between feature selection and feature extraction

3648-413: The recognition of another member of the same species Molecular recognition , the interaction between two or more molecules through non-covalent bonding Arts and entertainment [ edit ] " The Recognition ", a science fiction short story by J. G. Ballard The Recognitions , a 1955 postmodernist novel by William Gaddis Recognition , an EP by The Europeans Recognise (album) ,

3712-428: The skill of identifying aircraft on sight Revenue recognition , in accrual accounting See also [ edit ] [REDACTED] Wikiquote has quotations related to Recognition . All pages with titles beginning with Recognition All pages with titles containing Recognition Topics referred to by the same term [REDACTED] This disambiguation page lists articles associated with

3776-597: The terminology is different. In community ecology , the term classification is used to refer to what is commonly known as "clustering". The piece of input data for which an output value is generated is formally termed an instance . The instance is formally described by a vector of features, which together constitute a description of all known characteristics of the instance. These feature vectors can be seen as defining points in an appropriate multidimensional space , and methods for manipulating vectors in vector spaces can be correspondingly applied to them, such as computing

3840-674: The title Recognition . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=Recognition&oldid=1185411027 " Category : Disambiguation pages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages Pattern recognition Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have

3904-463: The training data, and generalize as well as possible to new data (usually, this means being as simple as possible, for some technical definition of "simple", in accordance with Occam's Razor , discussed below). Unsupervised learning , on the other hand, assumes training data that has not been hand-labeled, and attempts to find inherent patterns in the data that can then be used to determine the correct output value for new data instances. A combination of

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3968-467: The two that has been explored is semi-supervised learning , which uses a combination of labeled and unlabeled data (typically a small set of labeled data combined with a large amount of unlabeled data). In cases of unsupervised learning, there may be no training data at all. Sometimes different terms are used to describe the corresponding supervised and unsupervised learning procedures for the same type of output. The unsupervised equivalent of classification

4032-478: The user, which are then a priori. Moreover, experience quantified as a priori parameter values can be weighted with empirical observations – using e.g., the Beta- ( conjugate prior ) and Dirichlet-distributions . The Bayesian approach facilitates a seamless intermixing between expert knowledge in the form of subjective probabilities, and objective observations. Probabilistic pattern classifiers can be used according to

4096-517: Was captured with stylus and overlay starting in 1990. The strokes, speed, relative min, relative max, acceleration and pressure is used to uniquely identify and confirm identity. Banks were first offered this technology, but were content to collect from the FDIC for any bank fraud and did not want to inconvenience customers. Pattern recognition has many real-world applications in image processing. Some examples include: In psychology, pattern recognition

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