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Relevant bibliographies by topics / Class probability

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Academic literature on the topic 'Class probability'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Class probability"

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Młotkowski, Wojciech. "A class of probability measures." Journal of Computational and Applied Mathematics 133, no. 1-2 (2001): 694. http://dx.doi.org/10.1016/s0377-0427(00)00724-x.

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Lehnigk, S. H., and G. F. Roach. "On a class of probability distributions." Mathematical Methods in the Applied Sciences 9, no. 1 (1987): 210–19. http://dx.doi.org/10.1002/mma.1670090116.

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Simon, Richard. "Class probability estimation for medical studies." Biometrical Journal 56, no. 4 (2014): 597–600. http://dx.doi.org/10.1002/bimj.201300296.

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Kwok, J. T., I. W. H. Tsang, and J. M. Zurada. "A Class of Single-Class Minimax Probability Machines for Novelty Detection." IEEE Transactions on Neural Networks 18, no. 3 (2007): 778–85. http://dx.doi.org/10.1109/tnn.2007.891191.

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JIANG, LIANGXIAO, CHAOQUN LI, and ZHIHUA CAI. "DECISION TREE WITH BETTER CLASS PROBABILITY ESTIMATION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (2009): 745–63. http://dx.doi.org/10.1142/s0218001409007296.

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Traditionally, the performance of a classifier is measured by its classification accuracy or error rate. In fact, probability-based classifiers also produce the class probability estimation (the probability that a test instance belongs to the predicted class). This information is often ignored in classification, as long as the class with the highest class probability estimation is identical to the actual class. In many data mining applications, however, classification accuracy and error rate are not enough. For example, in direct marketing, we often need to deploy different promotion strategies to customers with different likelihood (class probability) of buying some products. Thus, accurate class probability estimations are often required to make optimal decisions. In this paper, we firstly review some state-of-the-art probability-based classifiers and empirically investigate their class probability estimation performance. From our experimental results, we can draw a conclusion: C4.4 is an attractive algorithm for class probability estimation. Then, we present a locally weighted version of C4.4 to scale up its class probability estimation performance by combining locally weighted learning with C4.4. We call our improved algorithm locally weighted C4.4, simply LWC4.4. We experimentally test LWC4.4 using the whole 36 UCI data sets selected by Weka. The experimental results show that LWC4.4 significantly outperforms C4.4 in terms of class probability estimation.

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Woon Jeung Park and Rhee Man Kil. "Pattern Classification With Class Probability Output Network." IEEE Transactions on Neural Networks 20, no. 10 (2009): 1659–73. http://dx.doi.org/10.1109/tnn.2009.2029103.

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Khoshyaran, Mahkame. "On a Class of Universal Probability Spaces." Advances in Research 6, no. 5 (2016): 1–7. http://dx.doi.org/10.9734/air/2016/24109.

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Wallace, Byron C., and Issa J. Dahabreh. "Improving class probability estimates for imbalanced data." Knowledge and Information Systems 41, no. 1 (2013): 33–52. http://dx.doi.org/10.1007/s10115-013-0670-6.

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Shahbaz, Saman, Muhammad Qaiser Shahbaz, M. Ahsanullah, and Muhammad Mohsin. "On a new class of probability distributions." Applied Mathematics Letters 24, no. 4 (2011): 545–52. http://dx.doi.org/10.1016/j.aml.2010.11.010.

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Kalkanis, G., and G. V. Conroy. "Interval error estimators in class probability trees." Pattern Recognition Letters 17, no. 7 (1996): 705–12. http://dx.doi.org/10.1016/0167-8655(96)00035-9.

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Dissertations / Theses on the topic "Class probability"

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Silman, Timothy. "Race, class rank, and college admission probability." CONNECT TO ELECTRONIC THESIS, 2006. http://hdl.handle.net/1961/3616.

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Coyne, Michelle A. "Predicting Arrest Probability Across Time: A Test of Competing Perspectives." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306285.

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Ding, Xiqian, and 丁茜茜. "Some new statistical methods for a class of zero-truncated discrete distributions with applications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2015. http://hdl.handle.net/10722/211126.

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Counting data without zero category often occur in various _elds. Examples include days of hospital stay for patients, numbers of publication for tenure-tracked faculty in a university, numbers of tra_c violation for drivers during a certain period and so on. A class of zero-truncated discrete models such as zero-truncated Poisson, zero-truncated binomial and zero-truncated negative-binomial distributions are proposed in literature to model such count data. In this thesis, firstly, literature review is presented in Chapter 1 on a class of commonly used univariate zero-truncated discrete distributions. In Chapter 2, a unified method is proposed to derive the distribution of the sum of i.i.d. zero-truncated distribution random variables, which has important applications in the construction of the shortest Clopper-Person confidence intervals of parameters of interest and in the calculation of the exact p-value of a two-sided test for small sample sizes in one sample problem. These problems are discussed in Section 2.4. Then a novel expectation-maximization (EM) algorithm is developed for calculating the maximum likelihood estimates (MLEs) of parameters in general zero-truncated discrete distributions. An important feature of the proposed EM algorithm is that the latent variables and the observed variables are independent, which is unusual in general EM-type algorithms. In addition, a unified minorization-maximization (MM) algorithm for obtaining the MLEs of parameters in a class of zero-truncated discrete distributions is provided. The first objective of Chapter 3 is to propose the multivariate zero-truncated Charlier series (ZTCS) distribution by developing its important distributional properties, and providing efficient MLE methods via a novel data augmentation in the framework of the EM algorithm. Since the joint marginal distribution of any r-dimensional sub-vector of the multivariate ZTCS random vector of dimension m is an r-dimensional zero-deated Charlier series (ZDCS) distribution (1 6 r < m), it is the second objective of Chapter 3 to propose a new family of multivariate zero-adjusted Charlier series (ZACS) distributions (including the multivariate ZDCS distribution as a special member) with a more flexible correlation structure by accounting for both inflation and deflation at zero. The corresponding distributional properties are explored and the associated MLE method via EM algorithm is provided for analyzing correlated count data.<br>published_or_final_version<br>Statistics and Actuarial Science<br>Master<br>Master of Philosophy

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Karlsson, John. "A class of infinite dimensional stochastic processes with unbounded diffusion." Licentiate thesis, Linköpings universitet, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-96583.

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The aim of this work is to provide an introduction into the theory of infinite dimensional stochastic processes. The thesis contains the paper A class of infinite dimensional stochastic processes with unbounded diffusion written at Linköping University during 2012. The aim of that paper is to take results from the finite dimensional theory into the infinite dimensional case. This is done via the means of a coordinate representation. It is shown that for a certain kind of Dirichlet form with unbounded diffusion, we have properties such as closability, quasi-regularity, and existence of local first and second moment of the associated process. The starting chapters of this thesis contain the prerequisite theory for understanding the paper. It is my hope that any reader unfamiliar with the subject will find this thesis useful, as an introduction to the field of infinite dimensional processes.

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Fuller, Randetta Lynn. "A Comparative Study between the Standards of Learning and In-Class Grades." Digital Commons @ East Tennessee State University, 2010. https://dc.etsu.edu/etd/1737.

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We examined the Standards of Learning mathematics scores and in-class grades for a rural Virginia county public school system. We looked at third, fourth, fifth, sixth, and seventh grades as well as Algebra I, Algebra II, and Geometry classes. The purpose of this was to determine whether or not there is a strong correlation between the Standards of Learning and the students' in-class grades. Had a strong enough correlation between the Standards of Learning and in-class grades been found we would have used only the in-class grades to predict the Standard of Learning test scores. However, we found that the students' in-class grades are not the only predictor of the Standards of Learning test scores. With the coefficient of determination ranging from 6.8% to 84.4%, this indicates that at best 84.4% of variation in the response is explained by the model for Algebra II and at worst only 6.8% for Algebra I.

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Thaning, Max. "The Social Relativity of Higher Education : The Influence of Social Capital on the Probability of Commencing Tertiary Studies." Thesis, Stockholms universitet, Sociologiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-125338.

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The decision of whether to enter higher education or not is strongly structured by social background, i.e. parents’ social class. This paper examines if and to what extent enrolment in tertiary education also is related to social capital, and furthermore, if social capital can account for differences in social background. Two aspects of social capital are examined: extended network resources, operationalized as family acquaintances holding different occupations, and peer influences, assessed as the number of friends already engaged in higher educational studies. The sample consists of two subsequent surveys of Swedish adolescents, divided in three stratums based on parents’ country of birth: Iran, former Yugoslavia and Sweden. The findings suggest that resources and returns of extended social networks enhance the probability of enrolment to university studies. The results concerning peer influences seem to indicate an impact on the probability of university admission, however, there might be causal and analytical problems of measurement involved.

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Val, Petran. "BINOCULAR DEPTH PERCEPTION, PROBABILITY, FUZZY LOGIC, AND CONTINUOUS QUANTIFICATION OF UNIQUENESS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1504749439893027.

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Berlin, Daniel. "Multi-class Supervised Classification Techniques for High-dimensional Data: Applications to Vehicle Maintenance at Scania." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209257.

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In vehicle repairs, many times locating the cause of error could turn out more time consuming than the reparation itself. Hence a systematic way to accurately predict a fault causing part would constitute a valuable tool especially for errors difficult to diagnose. This thesis explores the predictive ability of Diagnostic Trouble Codes (DTC’s), produced by the electronic system on Scania vehicles, as indicators for fault causing parts. The statistical analysis is based on about 18800 observations of vehicles where both DTC’s and replaced parts could be identified during the period march 2016 - march 2017. Two different approaches of forming classes is evaluated. Many classes had only few observations and, to give the classifiers a fair chance, it is decided to omit observations of classes based on their frequency in data. After processing, the resulting data could comprise 1547 observations on 4168 features, demonstrating very high dimensionality and making it impossible to apply standard methods of large-sample statistical inference. Two procedures of supervised statistical learning, that are able to cope with high dimensionality and multiple classes, Support Vector Machines and Neural Networks are exploited and evaluated. The analysis showed that on data with 1547 observations of 4168 features (unique DTC’s) and 7 classes SVM yielded an average prediction accuracy of 79.4% compared to 75.4% using NN.The conclusion of the analysis is that DTC’s holds potential to be used as indicators for fault causing parts in a predictive model, but in order to increase prediction accuracy learning data needs improvements. Scope for future research to improve and expand the model, along with practical suggestions for exploiting supervised classifiers at Scania is provided. keywords: Statistical learning, Machine learning, Neural networks, Deep learning, Supervised learning, High dimensionality<br>Många gånger i samband med fordonsreparationer är felsökningen mer tidskrävande än själva reparationen. Således skulle en systematisk metod för att noggrant prediktera felkällan vara ett värdefullt verktyg för att diagnostisera reparationsåtgärder. I denna uppsats undersöks möjligheten att använda Diagnostic Trouble Codes (DTC:er), som genereras av de elektroniska systemen i Scanias fordon, som indikatorer för att peka ut felorsaken. Till grund för analysen användes ca 18800 observationer av fordon där både DTC:er samt utbytta delar kunnat identifieras under perioden mars 2016 - mars 2017. Två olika strategier för att generera klasser har utvärderats. Till många av klasserna fanns det endast ett fåtal observationer, och för att ge de prediktiva modellerna bra förutsättningar så användes endast klasser med tillräckligt många observationer i träningsdata. Efter bearbetning kunde data innehålla 1547 observationer 4168 attribut, vilket demonstrerar problemets höga dimensionalitet och gör det omöjligt att applicera standard metoder för statistisk analys på stora datamängder. Två metoder för övervakad statistisk inlärning, lämpliga för högdimensionell data med multipla klasser, Södvectormaskiner (SVM) samt Neurala Nätverk (NN) implementeras och deras resultat utvärderas. Analysen visade att på data med 1547 observationer av 4168 attribut (unika DTC:er) och 7 klasser kunde SVM prediktera observationer till klasserna med 79.4% noggrannhet jämfört med 75.4% för NN. De slutsatser som kunde dras av analysen var att DTC:er tycks ha potential att användas för att indikera felorsaker med en prediktiv modell, men att den data som ligger till grund för analysen bör förbättras för att öka noggrannheten i de prediktiva modellerna. Framtida forskningsmöjligheter för att ytterligare förbättra samt utveckla modellen, tillsammans med förslag för hur övervakade klassificerings modeller kan användas på Scnaia har identifierats.

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Nybrant, Arvid, and Henrik Rundberg. "Predicting Uncertainty in Financial Markets : -An empirical study on ARCH-class models ability to estimate Value at Risk." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352381.

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Value at Risk has over the last couple of decades become one of the most widely used measures of market risk. Several methods to compute this measure have been suggested. In this paper, we evaluate the use of the GARCH(1,1)-, EGARCH(1,1)- and the APARCH(1,1) model for estimation of this measure under the assumption that the conditional error distribution is normally-, t-, skewed t- and NIG-distributed respectively. For each model, the 95% and 99% one-day Value at Risk is computed using rolling out-of-sample forecasts for three equity indices. These forecasts are evaluated with Kupiec´s test for unconditional coverage test and Christoffersen’s test for conditional coverage. The results imply that the models generally perform well. The APARCH(1,1) model seems to be the most robust model. However, the GARCH(1,1) and the EGARCH(1,1) models also provide accurate predictions. The results indicate that the assumption of conditional distribution matters more for 99% than 95% Value at Risk. Generally, a leptokurtic distribution appears to be a sound choice for the conditional distribution.

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Raharjo, Tatok Raharjo. "The Effects of Age and Gender on Pedestrian Traffic Injuries: A Random Parameters and Latent Class Analysis." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6360.

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Pedestrians are vulnerable road users because they do not have any protection while they walk. They are unlike cyclists and motorcyclists who often have at least helmet protection and sometimes additional body protection (in the case of motorcyclists with body-armored jackets and pants). In the US, pedestrian fatalities are increasing and becoming an ever larger proportion of overall roadway fatalities (NHTSA, 2016), thus underscoring the need to study factors that influence pedestrian-injury severity and potentially develop appropriate countermeasures. One of the critical elements in the study of pedestrian-injury severities is to understand how injuries vary across age and gender ‒ two elements that have been shown to be critical injury determinants in past research. In the current research effort, 4829 police-reported pedestrian crashes from Chicago in 2011 and 2012 are used to estimate multinomial logit, mixed logit, and latent class logit models to study the effects of age and gender on resulting injury severities in pedestrian crashes. The results from these model estimations show that the injury severity level for older males, younger males, older females, and younger females are statistically different. Moreover, the overall findings also show that older males and older females are more likely to have higher injury-severity levels in many instances (if a crash occurs on city streets, state maintained urban roads, the primary cause of the crash is failing to yield right-of way, pedestrian entering/ leaving/ crossing is not at intersection, road surface condition is dry, and road functional class is a local road or street). The findings suggest that well-designed and well-placed crosswalks, small islands in two-way streets, narrow streets, clear road signs, provisions for resting places, and wide, flat sidewalks all have the potential to result in lower pedestrian-injury severities across age/gender combinations.

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Books on the topic "Class probability"

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The search for certainty: On the clash of science and philosophy of probability. World Scientific, 2009.

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Christian, Brucker, Monka Yvan, and Sigward Éric, eds. Mathématiques 2de: Édition 2014. Hatier, 2014.

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Gale, Thomson. Bliss Bibliographic Classification: Class AM/AX: Mathematics, Statistics and Probability. K.G. Saur (An Impint of Walter de Gruyter), 1995.

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Bowker-Saur. Class Am/Ax Mathematics, Statistics and Probability (Bliss Bibliographic Classification). 2nd ed. Bowker-Saur, 1993.

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Leitgeb, Hannes. Probability in Logic. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.35.

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This chapter is about probabilistic logics: that is, systems of logic in which logical consequence is defined in probabilistic terms. The chapter starts with some historical background, covering the longstanding association of the study of logic with probability. There follows a clarification of the terms to be used. Then these systems of probabilistic logic are classified and some key references given. Two chief classifications are presented. Then one class of probabilistic logics, those that derive from Ernest Adams’ work, is presented in more detail. In reviewing Adams’ work, six types of semantics are presented. These are examined via six case studies.

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Williams, J. Robert G. Probability and Nonclassical Logic. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.12.

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This chapter presents axioms for comparative conditional probability relations. The axioms presented here are more general than usual. Each comparative relation is a weak partial order on pairs of sentences but need not be a complete order relation. The axioms for these comparative relations are probabilistically sound for the broad class of conditional probability functions known as Popper functions. Furthermore, these axioms are probabilistically complete. Arguably, the notion of comparative conditional probability provides a foundation for Bayesian confirmation theory. Bayesian confirmation functions are overly precise probabilistic representations of the more fundamental logic of comparative support. The most important features of evidential support are captured by comparative relationships among argument strengths, realized by the comparative support relations and their logic.

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Gelman, Andrew, and Deborah Nolan. Problems and projects in probability. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0019.

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When probability is taught as a separate unit, not as part of a statistics course, then it makes sense to include exercises and examples that are motivated from mathematics rather than from applications. This chapter illustrates one way to promote active student involvement in a more advanced class. The material is designed to accommodate various mathematical backgrounds and to challenge all students. There are essentially three basic aspects to our mathematical probability courses: we teach in an interactive seminar style; we give students many challenging problems; and we require students to work on longer projects where they derive complicated results step by step. This chapter provides examples of many different kinds of problems, such as challenging questions on Buffon’s needle and noodle, a structured project on arcsine laws, an unstructured project on random permutations, and reading research paper in probability.

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Borodin, Alexei, and Leonid Petrov. Integrable probability: stochastic vertex models and symmetric functions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0002.

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This chapter presents the study of a homogeneous stochastic higher spin six-vertex model in a quadrant. For this model concise integral representations for multipoint q-moments of the height function and for the q-correlation functions are derived. At least in the case of the step initial condition, these formulas degenerate in appropriate limits to many known formulas of such type for integrable probabilistic systems in the (1+1)d KPZ universality class, including the stochastic six-vertex model, ASEP, various q-TASEPs, and associated zero-range processes. The arguments are largely based on properties of a family of symmetric rational functions that can be defined as partition functions of the higher spin six-vertex model for suitable domains; they generalize classical Hall–Littlewood and Schur polynomials. A key role is played by Cauchy-like summation identities for these functions, which are obtained as a direct corollary of the Yang–Baxter equation for the higher spin six-vertex model.

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Dietrich, Franz, and Christian List. Probabilistic Opinion Pooling. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.37.

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Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This chapter is a review of several proposed solutions to this problem, focusing on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). Axiomatic characterizations of each class of pooling functions are presented (most characterizations are classic results, but one is new), with the argument that linear pooling can be justified “procedurally” but not “epistemically”, while the other two pooling methods can be justified “epistemically”. The choice between them, in turn, depends on whether the individuals' probability assignments are based on shared information or on private information. In conclusion a number of other pooling methods are mentioned.

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Kravtsov, Vladimir. Heavy-tailed random matrices. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.13.

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This article considers non-Gaussian random matrices consisting of random variables with heavy-tailed probability distributions. In probability theory heavy tails of distributions describe rare but violent events which usually have a dominant influence on the statistics. Furthermore, they completely change the universal properties of eigenvalues and eigenvectors of random matrices. This article focuses on the universal macroscopic properties of Wigner matrices belonging to the Lévy basin of attraction, matrices representing stable free random variables, and a class of heavy-tailed matrices obtained by parametric deformations of standard ensembles. It first examines the properties of heavy-tailed symmetric matrices known as Wigner–Lévy matrices before discussing free random variables and free Lévy matrices as well as heavy-tailed deformations. In particular, it describes random matrix ensembles obtained from standard ensembles by a reweighting of the probability measure. It also analyses several matrix models belonging to heavy-tailed random matrices and presents methods for integrating them.

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Book chapters on the topic "Class probability"

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Frank, Eibe, and Mark Hall. "Visualizing Class Probability Estimators." In Knowledge Discovery in Databases: PKDD 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39804-2_17.

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Collevecchio, Andrea, and Robert C. Griffiths. "A Class of Random Walks on the Hypercube." In Progress in Probability. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60754-8_13.

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AL-Hussaini, Essam K., and Mohammad Ahsanullah. "Class of Exponentiated Distributions Introduction." In Atlantis Studies in Probability and Statistics. Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-079-9_1.

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Frank, Eibe, and Remco R. Bouckaert. "Conditional Density Estimation with Class Probability Estimators." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05224-8_7.

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Marcus, Michael B., and Jay Rosen. "Continuity Conditions for a Class of Second-order Permanental Chaoses." In High Dimensional Probability VI. Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0490-5_15.

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Parthasarathy, K. R., and Kalyan B. Sinha. "Representation of a class of quantum martingales II." In Quantum Probability and Applications III. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0078066.

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Darling, R. W. R., and Arunava Mukherjea. "Discrete Time Voter Models: A Class of Stochastic Automata." In Probability Measures on Groups X. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2364-6_6.

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Szeidl, L., and V. M. Zolotarev. "Analytical Representation of Limit Distributions for a Class of Random Symmetric Polynomials." In Probability Theory and Applications. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2817-9_12.

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Guerra, Francesco. "Carlen processes: A new class of diffusions with singular drifts." In Quantum Probability and Applications II. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074478.

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Wang, Hongqiao, Yanning Cai, Shicheng Wang, Guangyuan Fu, and Linlin Li. "Classification Probability Estimation Based Multi-Class Image Retrieval." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37832-4_36.

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Conference papers on the topic "Class probability"

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Młotkowski, Wojciech. "Some class of polynomial hypergroups." In Quantum Probability. Institute of Mathematics Polish Academy of Sciences, 2006. http://dx.doi.org/10.4064/bc73-0-28.

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Dang, Tat-Dat, and Ha-Nam Nguyen. "Multi-class Minimax Probability Machine." In 2009 International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2009. http://dx.doi.org/10.1109/kse.2009.46.

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Wang, X. H., S. Albeverio, L. Cattaneo, and S. M. Fei. "Local Equivalence for a Class of Bipartite Quantum Mixed States." In FOUNDATIONS OF PROBABILITY AND PHYSICS - 4. AIP, 2007. http://dx.doi.org/10.1063/1.2713470.

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Meng, Zhang, Bai Yu, Zhang Yan, and Wang Xiaojing. "Promoting Effects of Network Class in Probability." In 2010 2nd International Conference on Information Technology and Computer Science (ITCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/itcs.2010.142.

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O'Brien, J. "Correlated probability fusion for multiple class discrimination." In 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251). IEEE, 1999. http://dx.doi.org/10.1109/idc.1999.754218.

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O'Brien, Deirdre B., Maya R. Gupta, and Robert M. Gray. "Cost-sensitive multi-class classification from probability estimates." In the 25th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390246.

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Clark, Stephen, and David Weir. "Class-based probability estimation using a semantic hierarchy." In Second meeting of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073336.1073349.

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Wang, Xiaoh. "Posterior Probability Reconstruction for Multi-Class Support Vector Machines." In 2008 International Conference on Computational Intelligence and Security (CIS). IEEE, 2008. http://dx.doi.org/10.1109/cis.2008.10.

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Ping He, Guofeng Pan, Yatong Zhou, and Hongdong Zhao. "Facial expression recognition using multi-class minimax probability machine." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4592839.

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Charalambous, Charalambos D., and Ioanna Ioannou. "Minimizing outage probability for a class of MIMO channels." In 2009 11th Canadian Workshop on Information Theory (CWIT). IEEE, 2009. http://dx.doi.org/10.1109/cwit.2009.5069520.

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Reports on the topic "Class probability"

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Lehnigk, S. H., and H. P. Dudel. Diagnostic Parameter Determination for a Class of Three-Parameter Probability Distributions. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada203706.

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