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Multiple sequence alignment : Alignment-free sequence analysis Cladistics Generalized tree alignment Multiple sequence alignment viewers PANDIT, a biological database covering protein domains Phylogenetics Sequence alignment software Structural alignment |
Multiple sequence alignment : ExPASy sequence alignment tools Archived Multiple Alignment Resource Page – from the Virtual School of Natural Sciences Tools for Multiple Alignments – from Pôle Bioinformatique Lyonnais An entry point to clustal servers and information An entry point to the main T-Coffee servers An entry ... |
Optimistic knowledge gradient : In statistics, the optimistic knowledge gradient is a smart decision-making strategy developed by Xi Chen, Qihang Lin and Dengyong Zhou in 2013 to help solve complex problems in crowdsourced data labeling (a form of optimal computing budget allocation problem). In crowdsourcing, multiple... |
Optimistic knowledge gradient : The optimal computing budget allocation problem is formulated as a Bayesian Markov decision process(MDP) and is solved by using the dynamic programming (DP) algorithm where the Optimistic knowledge gradient policy is used to solve the computationally intractable of the dynamic programmin... |
Optimistic knowledge gradient : We want to finish this labeling tasks rely on the power of the crowd hopefully. For example, suppose we want to identify a picture according to the people in a picture is adult or not, this is a Bernoulli labeling problem, and all of us can do in one or two seconds, this is an easy task ... |
Optimistic knowledge gradient : Before showing the mathematic model, the paper mentions what kinds of challenges we are facing. |
Optimistic knowledge gradient : For the mathematical model, we have the K items, i = , and total budget is T and we assume each label cost 1 so we are going to have T labels eventually. We assume each items has true label Z i which positive or negative, this binomial cases and we can extended to multiple class, labe... |
PageRank : PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page. PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number and quality of... |
PageRank : PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotatio... |
PageRank : The eigenvalue problem behind PageRank's algorithm was independently rediscovered and reused in many scoring problems. In 1895, Edmund Landau suggested using it for determining the winner of a chess tournament. The eigenvalue problem was also suggested in 1976 by Gabriel Pinski and Francis Narin, who worked ... |
PageRank : The PageRank algorithm outputs a probability distribution used to represent the likelihood that a person randomly clicking on links will arrive at any particular page. PageRank can be calculated for collections of documents of any size. It is assumed in several research papers that the distribution is evenly... |
PageRank : The mathematics of PageRank are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It is used for systems analysis of road networks, and in biology, chemi... |
PageRank : In early 2005, Google implemented a new value, "nofollow", for the rel attribute of HTML link and anchor elements, so that website developers and bloggers can make links that Google will not consider for the purposes of PageRank—they are links that no longer constitute a "vote" in the PageRank system. The no... |
PageRank : Attention inequality CheiRank Domain authority EigenTrust — a decentralized PageRank algorithm Google bombing Google Hummingbird Google matrix Google Panda Google Penguin Google Search Hilltop algorithm Katz centrality – a 1953 scheme closely related to pagerank Link building Search engine optimization SimRa... |
PageRank : Original PageRank U.S. Patent—Method for node ranking in a linked database Archived 2014-08-29 at the Wayback Machine—Patent number 6,285,999—September 4, 2001 PageRank U.S. Patent—Method for scoring documents in a linked database—Patent number 6,799,176—September 28, 2004 PageRank U.S. Patent—Method for nod... |
PageRank : Algorithms by Google Our products and services by Google How Google Finds Your Needle in the Web's Haystack by the American Mathematical Society |
Path dependence : Path dependence is a concept in the social sciences, referring to processes where past events or decisions constrain later events or decisions. It can be used to refer to outcomes at a single point in time or to long-run equilibria of a process. Path dependence has been used to describe institutions, ... |
Path dependence : Path dependence theory was originally developed by economists to explain technology adoption processes and industry evolution. The theoretical ideas have had a strong influence on evolutionary economics. A common expression of the concept is the claim that predictable amplifications of small differenc... |
Path dependence : A general type of path dependence is a typological vestige. In typography, for example, some customs persist, although the reason for their existence no longer applies; for example, the placement of the period inside a quotation in U.S. spelling. In metal type, pieces of terminal punctuation, such as ... |
Path dependence : Critical juncture theory Imprinting (organizational theory) Innovation butterfly Historicism Network effect Opportunity cost Ratchet effect Tyranny of small decisions |
Path dependence : Arrow, Kenneth J. (1963), 2nd ed. Social Choice and Individual Values. Yale University Press, New Haven, pp. 119–120 (constitutional transitivity as alternative to path dependence on the status quo). Arthur, W. Brian (1994), Increasing Returns and Path Dependence in the Economy. University of Michigan... |
Population process : In applied probability, a population process is a Markov chain in which the state of the chain is analogous to the number of individuals in a population (0, 1, 2, etc.), and changes to the state are analogous to the addition or removal of individuals from the population. Typical population processe... |
Population process : Moran process == References == |
Quantum Markov chain : In mathematics, the quantum Markov chain is a reformulation of the ideas of a classical Markov chain, replacing the classical definitions of probability with quantum probability. |
Quantum Markov chain : Very roughly, the theory of a quantum Markov chain resembles that of a measure-many automaton, with some important substitutions: the initial state is to be replaced by a density matrix, and the projection operators are to be replaced by positive operator valued measures. |
Quantum Markov chain : More precisely, a quantum Markov chain is a pair ( E , ρ ) with ρ a density matrix and E a quantum channel such that E : B ⊗ B → B \otimes \to is a completely positive trace-preserving map, and B a C*-algebra of bounded operators. The pair must obey the quantum Markov condition, that Tr ρ ... |
Quantum Markov chain : Gudder, Stanley. "Quantum Markov chains." Journal of Mathematical Physics 49.7 (2008): 072105. |
Queueing theory : Queueing theory is the mathematical study of waiting lines, or queues. A queueing model is constructed so that queue lengths and waiting time can be predicted. Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions abou... |
Queueing theory : The spelling "queueing" over "queuing" is typically encountered in the academic research field. In fact, one of the flagship journals of the field is Queueing Systems. |
Queueing theory : Queueing theory is one of the major areas of study in the discipline of management science. Through management science, businesses are able to solve a variety of problems using different scientific and mathematical approaches. Queueing analysis is the probabilistic analysis of waiting lines, and thus ... |
Queueing theory : A queue or queueing node can be thought of as nearly a black box. Jobs (also called customers or requests, depending on the field) arrive to the queue, possibly wait some time, take some time being processed, and then depart from the queue. However, the queueing node is not quite a pure black box sinc... |
Queueing theory : In 1909, Agner Krarup Erlang, a Danish engineer who worked for the Copenhagen Telephone Exchange, published the first paper on what would now be called queueing theory. He modeled the number of telephone calls arriving at an exchange by a Poisson process and solved the M/D/1 queue in 1917 and M/D/k qu... |
Queueing theory : Various scheduling policies can be used at queueing nodes: First in, first out Also called first-come, first-served (FCFS), this principle states that customers are served one at a time and that the customer that has been waiting the longest is served first. Last in, first out This principle also serv... |
Queueing theory : Queue networks are systems in which multiple queues are connected by customer routing. When a customer is serviced at one node, it can join another node and queue for service, or leave the network. For networks of m nodes, the state of the system can be described by an m–dimensional vector (x1, x2, ..... |
Queueing theory : Gross, Donald; Carl M. Harris (1998). Fundamentals of Queueing Theory. Wiley. ISBN 978-0-471-32812-4. Online Zukerman, Moshe (2013). Introduction to Queueing Theory and Stochastic Teletraffic Models (PDF). arXiv:1307.2968. Deitel, Harvey M. (1984) [1982]. An introduction to operating systems (revisite... |
Queueing theory : Teknomo's Queueing theory tutorial and calculators Virtamo's Queueing Theory Course Myron Hlynka's Queueing Theory Page LINE: a general-purpose engine to solve queueing models |
Random surfing model : The random surfing model is a graph model which describes the probability of a random user visiting a web page. The model attempts to predict the chance that a random internet surfer will arrive at a page by either clicking a link or by accessing the site directly, for example by directly enterin... |
Random surfing model : A user navigates the internet in two primary ways; the user may access a site directly by entering the site's URL or clicking a bookmark, or the user may use a series of hyperlinks to get to the desired page. The random surfer model assumes that the link which the user selects next is picked at r... |
Random surfing model : In the random surfing model, webgraphs are presented as a sequence of directed graphs G t , t = 1 , 2 , … ,t=1,2,\ldots such that a graph G t has t vertices and t edges. The process of defining graphs is parameterized with a probability p , thus we let q = 1 − p . Nodes of the model arrive ... |
Random surfing model : There are some caveats to the standard random surfer model, one of which is that the model ignores the content of the sites which users select – since the model assumes links are selected at random. Because users tend to have a goal in mind when surfing the internet, the content of the linked sit... |
Random surfing model : The normalized eigenvector centrality combined with random surfer model's assumption of random jumps created the foundation of Google's PageRank algorithm. |
Random surfing model : Avrim Blum PageRank Webgraph |
Random surfing model : Case study on random web surfers Data Mining and Analysis: Fundamental Concepts and Algorithms is freely available to download for personal use here Microsoft research on PageRank and the Random Surfer Model Paper on how Google web search implements PageRank to find relevant search results |
Snakes and ladders : Snakes and ladders is a board game for two or more players regarded today as a worldwide classic. The game originated in ancient India as Moksha Patam, and was brought to the United Kingdom in the 1890s. It is played on a game board with numbered, gridded squares. A number of "ladders" and "snakes"... |
Snakes and ladders : The size of the grid varies, but is most commonly 8×8, 10×10 or 12×12 squares. Boards have snakes and ladders starting and ending on different squares; both factors affect the duration of play. Each player is represented by a distinct game piece token. A single die is rolled to determine random mov... |
Snakes and ladders : Snakes and ladders originated as part of a family of Indian dice board games that included gyan chauper and pachisi (known in English as Ludo and Parcheesi). It made its way to England and was sold as "Snakes and Ladders", then the basic concept was introduced in the United States as Chutes and Lad... |
Snakes and ladders : Each player starts with a token on the starting square (usually the "1" grid square in the bottom left corner, or simply, at the edge of the board next to the "1" grid square). Players take turns rolling a single die to move their token by the number of squares indicated by the die rolled. Tokens f... |
Snakes and ladders : The most widely known edition of snakes and ladders in the United States is Chutes and Ladders, released by Milton Bradley in 1943. The playground setting replaced the snakes, which were thought to be disliked by children at the time. It is played on a 10x10 board, and players advance their pieces ... |
Snakes and ladders : Any version of snakes and ladders can be represented exactly as an absorbing Markov chain, since from any square the odds of moving to any other square are fixed and independent of any previous game history. The Milton Bradley version of Chutes and Ladders has 100 squares, with 19 chutes and ladder... |
Snakes and ladders : The phrase "back to square one" originated in the game of snakes and ladders, or at least was influenced by it – the earliest attestation of the phrase refers to the game: "Withal he has the problem of maintaining the interest of the reader who is always being sent back to square one in a sort of i... |
Snakes and ladders : Bibliography Augustyn, Frederick J (2004). Dictionary of toys and games in American popular culture. Haworth Press. ISBN 0-7890-1504-8. Parlett, David (1999). "Snakes & Ladders". The Oxford History of Board Games. Oxford University Press. pp. 91–94. ISBN 0-19-212998-8. Tatz, Mark; Kent, Jody (1977)... |
Snakes and ladders : Berlekamp, Elwyn R; Conway, John H; Guy, Richard K (1982). Winning Ways for Your Mathematical Plays. Academic Press. ISBN 0-12-091150-7. Shimkhada, Deepak (1983), "A Preliminary Study of the Game of Karma in India, Nepal, and Tibet" in Artibus Asiae 44:4, pp. 308–322. Topsfield, Andrew (1985), "The... |
Snakes and ladders : Media related to Snakes and ladders at Wikimedia Commons |
Stochastic cellular automaton : Stochastic cellular automata or probabilistic cellular automata (PCA) or random cellular automata or locally interacting Markov chains are an important extension of cellular automaton. Cellular automata are a discrete-time dynamical system of interacting entities, whose state is discrete... |
Stochastic cellular automaton : As discrete-time Markov process, PCA are defined on a product space E = ∏ k ∈ G S k S_ (cartesian product) where G is a finite or infinite graph, like Z and where S k is a finite space, like for instance S k = =\ or S k = =\ . The transition probability has a product form P ( d σ |... |
Stochastic cellular automaton : Almeida, R. M.; Macau, E. E. N. (2010), "Stochastic cellular automata model for wildland fire spread dynamics", 9th Brazilian Conference on Dynamics, Control and their Applications, June 7–11, 2010, vol. 285, p. 012038, doi:10.1088/1742-6596/285/1/012038. Clarke, K. C.; Hoppen, S. (1997)... |
Stochastic matrix : In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability.: 10 It is also called a probability matrix, transition matrix, substitution matrix, or Markov matrix. The stochastic... |
Stochastic matrix : The stochastic matrix was developed alongside the Markov chain by Andrey Markov, a Russian mathematician and professor at St. Petersburg University who first published on the topic in 1906. His initial intended uses were for linguistic analysis and other mathematical subjects like card shuffling, bu... |
Stochastic matrix : A stochastic matrix describes a Markov chain Xt over a finite state space S with cardinality α. If the probability of moving from i to j in one time step is Pr(j|i) = Pi,j, the stochastic matrix P is given by using Pi,j as the i-th row and j-th column element, e.g., P = [ P 1 , 1 P 1 , 2 … P 1 , j …... |
Stochastic matrix : Suppose there is a timer and a row of five adjacent boxes. At time zero, a cat is in the first box, and a mouse is in the fifth box. The cat and the mouse both jump to a random adjacent box when the timer advances. For example, if the cat is in the second box and the mouse is in the fourth, the prob... |
Stochastic matrix : Density matrix Markov kernel, the equivalent of a stochastic matrix over a continuous state space Matrix difference equation Models of DNA evolution Muirhead's inequality Probabilistic automaton Transition rate matrix, used to generalize the stochastic matrix to continuous time == References == |
Switching Kalman filter : The switching Kalman filtering (SKF) method is a variant of the Kalman filter. In its generalised form, it is often attributed to Kevin P. Murphy, but related switching state-space models have been in use. |
Switching Kalman filter : Applications of the switching Kalman filter include: Brain–computer interfaces and neural decoding, real-time decoding for continuous neural-prosthetic control, and sensorimotor learning in humans. It also has application in econometrics, signal processing, tracking, computer vision, etc. It i... |
Switching Kalman filter : There are several variants of SKF discussed in. |
Variable-order Bayesian network : Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models. VOBN models are used in machine learning in general and have shown great potential in bioinformatics applications. These models extend ... |
Variable-order Bayesian network : Markov chain Examples of Markov chains Variable order Markov models Markov process Markov chain Monte Carlo Semi-Markov process Artificial intelligence |
Variable-order Bayesian network : VOMBAT: https://www2.informatik.uni-halle.de:8443/VOMBAT/ |
Variable-order Markov model : In the mathematical theory of stochastic processes, variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models. In contrast to the Markov chain models, where each random variable in a sequence with a Markov property depends on a fixed... |
Variable-order Markov model : Consider for example a sequence of random variables, each of which takes a value from the ternary alphabet . Specifically, consider the string constructed from infinite concatenations of the sub-string aaabc: aaabcaaabcaaabcaaabc…aaabc. The VOM model of maximal order 2 can approximate the ... |
Variable-order Markov model : Let A be a state space (finite alphabet) of size | A | . Consider a sequence with the Markov property x 1 n = x 1 x 2 … x n ^=x_x_\dots x_ of n realizations of random variables, where x i ∈ A \in A is the state (symbol) at position i ( 1 ≤ i ≤ n ) , and the concatenation of states x i a... |
Variable-order Markov model : Various efficient algorithms have been devised for estimating the parameters of the VOM model. VOM models have been successfully applied to areas such as machine learning, information theory and bioinformatics, including specific applications such as coding and data compression, document c... |
Variable-order Markov model : Stochastic chains with memory of variable length Examples of Markov chains Variable order Bayesian network Markov process Markov chain Monte Carlo Semi-Markov process Artificial intelligence == References == |
Viterbi algorithm : The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events. This is done especially in the context of Markov information source... |
Viterbi algorithm : The Viterbi algorithm is named after Andrew Viterbi, who proposed it in 1967 as a decoding algorithm for convolutional codes over noisy digital communication links. It has, however, a history of multiple invention, with at least seven independent discoveries, including those by Viterbi, Needleman an... |
Viterbi algorithm : Given a hidden Markov model with a set of hidden states S and a sequence of T observations o 0 , o 1 , … , o T − 1 ,o_,\dots ,o_ , the Viterbi algorithm finds the most likely sequence of states that could have produced those observations. At each time step t , the algorithm solves the subproblem ... |
Viterbi algorithm : function Viterbi(states, init, trans, emit, obs) is input states: S hidden states input init: initial probabilities of each state input trans: S × S transition matrix input emit: S × O emission matrix input obs: sequence of T observations prob ← T × S matrix of zeroes prev ← empty T × S matrix for e... |
Viterbi algorithm : A doctor wishes to determine whether patients are healthy or have a fever. The only information the doctor can obtain is by asking patients how they feel. The patients may report that they either feel normal, dizzy, or cold. It is believed that the health condition of the patients operates as a disc... |
Viterbi algorithm : A generalization of the Viterbi algorithm, termed the max-sum algorithm (or max-product algorithm) can be used to find the most likely assignment of all or some subset of latent variables in a large number of graphical models, e.g. Bayesian networks, Markov random fields and conditional random field... |
Viterbi algorithm : The soft output Viterbi algorithm (SOVA) is a variant of the classical Viterbi algorithm. SOVA differs from the classical Viterbi algorithm in that it uses a modified path metric which takes into account the a priori probabilities of the input symbols, and produces a soft output indicating the relia... |
Viterbi algorithm : Expectation–maximization algorithm Baum–Welch algorithm Forward-backward algorithm Forward algorithm Error-correcting code Viterbi decoder Hidden Markov model Part-of-speech tagging A* search algorithm |
Viterbi algorithm : Viterbi AJ (April 1967). "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm". IEEE Transactions on Information Theory. 13 (2): 260–269. doi:10.1109/TIT.1967.1054010. (note: the Viterbi decoding algorithm is described in section IV.) Subscription required. Feldman ... |
Viterbi algorithm : Implementations in Java, F#, Clojure, C# on Wikibooks Tutorial on convolutional coding with viterbi decoding, by Chip Fleming A tutorial for a Hidden Markov Model toolkit (implemented in C) that contains a description of the Viterbi algorithm Viterbi algorithm by Dr. Andrew J. Viterbi (scholarpedia.... |
Facial recognition system : A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facia... |
Facial recognition system : Automated facial recognition was pioneered in the 1960s by Woody Bledsoe, Helen Chan Wolf, and Charles Bisson, whose work focused on teaching computers to recognize human faces. Their early facial recognition project was dubbed "man-machine" because a human first needed to establish the coor... |
Facial recognition system : While humans can recognize faces without much effort, facial recognition is a challenging pattern recognition problem in computing. Facial recognition systems attempt to identify a human face, which is three-dimensional and changes in appearance with lighting and facial expression, based on ... |
Facial recognition system : In the 18th and 19th century, the belief that facial expressions revealed the moral worth or true inner state of a human was widespread and physiognomy was a respected science in the Western world. From the early 19th century onwards photography was used in the physiognomic analysis of facia... |
Facial recognition system : The development of anti-facial recognition technology is effectively an arms race between privacy researchers and big data companies. Big data companies increasingly use convolutional AI technology to create ever more advanced facial recognition models. Solutions to block facial recognition ... |
Facial recognition system : Lists List of computer vision topics List of emerging technologiesOutline of artificial intelligence |
Facial recognition system : Farokhi, Sajad; Shamsuddin, Siti Mariyam; Flusser, Jan; Sheikh, U.U; Khansari, Mohammad; Jafari-Khouzani, Kourosh (2014). "Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform". Digital Signal Processing. 31 (1): 13–27. Bibcode:2014DSP....31.... |
Facial recognition system : Media related to Facial recognition system at Wikimedia Commons A Photometric Stereo Approach to Face Recognition (master's thesis). The University of the West of England, Bristol. |
FERET (facial recognition technology) : The Facial Recognition Technology (FERET) program was a government-sponsored project that aimed to create a large, automatic face-recognition system for intelligence, security, and law enforcement purposes. The program began in 1993 under the combined leadership of Dr. Harry Wech... |
FERET (facial recognition technology) : The origin of facial recognition technology is largely attributed to Woodrow Wilson Bledsoe and his work in the 1960s, when he developed a system to identify faces from a database of thousands of photographs. The FERET program first began as a way to unify a large body of face-re... |
FERET (facial recognition technology) : FERET NIST Website Color FERET Database FERET NIST Documents |
FERET database : The Facial Recognition Technology (FERET) database is a dataset used for facial recognition system evaluation as part of the Face Recognition Technology (FERET) program. It was first established in 1993 under a collaborative effort between Harry Wechsler at George Mason University and Jonathon Phillips... |
FERET database : Official website about the gray-scale version Official website about the color version More official information IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 22, NO. 10, October 2000 More documents about FERET |
Handwriting recognition : Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed ... |
Handwriting recognition : Offline handwriting recognition involves the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting. Offline handwriting recognition is com... |
Handwriting recognition : Online handwriting recognition involves the automatic conversion of text as it is written on a special digitizer or PDA, where a sensor picks up the pen-tip movements as well as pen-up/pen-down switching. This kind of data is known as digital ink and can be regarded as a digital representation... |
Handwriting recognition : Handwriting recognition has an active community of academics studying it. The biggest conferences for handwriting recognition are the International Conference on Frontiers in Handwriting Recognition (ICFHR), held in even-numbered years, and the International Conference on Document Analysis and... |
Handwriting recognition : Since 2009, the recurrent neural networks and deep feedforward neural networks developed in the research group of Jürgen Schmidhuber at the Swiss AI Lab IDSIA have won several international handwriting competitions. In particular, the bi-directional and multi-dimensional Long short-term memory... |
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