Relevant bibliographies by topics / Representation analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Representation analysis'

Author: Grafiati
Published: 24 May 2025
Last updated: 19 July 2025

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Journal articles on the topic "Representation analysis"

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Popal, Haroon, Yin Wang, and Ingrid R. Olson. "A Guide to Representational Similarity Analysis for Social Neuroscience." Social Cognitive and Affective Neuroscience 14, no. 11 (2019): 1243–53. http://dx.doi.org/10.1093/scan/nsz099.

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Abstract Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.
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Zahriah, Zahriah, Mujibul Rijal, and Fera Annisa. "An Analysis of Students' Ability to Solve Problems Related to Newton's Law of Gravitation from a Multirepresentational Perspective." Journal of Aceh Physics Society 14, no. 1 (2025): 23–32. https://doi.org/10.24815/jacps.v14i1.44449.

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Students' skills in solving physics problems with a correct and in-depth understanding of concepts are skills that they should have in facing life in the 21st Century. One way to identify students who can solve problems is by looking at their ability to represent physics problems in suitable formats. different things which are called multiple representations. This research aims to analyze students' abilities in solving physics problems based on multi-representational aspects, in this case, what is looked at are aspects of verbal, visual, symbolic, and mathematical representation. The data collection technique used was a test with test questions in the form of essays with cognitive levels C3, C4, and C5. The research results show that the overall average score for students' ability to solve problems is 51.7, with a percentage of verbal representation of 73.13%, visual representation of 57.25%, symbol representation of 63.64%, and mathematical representation of 65. 45%. Thus, it can be concluded that overall students' physics problem-solving abilities are in the medium category, abilities based on verbal representation aspects are in the high category, visual representations are in the medium category, symbol representations are in the medium category, and mathematical aspects are in the high category.
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Ruliani, Iva Desi, Nizaruddin Nizaruddin, and Yanuar Hery Murtianto. "Profile Analysis of Mathematical Problem Solving Abilities with Krulik & Rudnick Stages Judging from Medium Visual Representation." JIPM (Jurnal Ilmiah Pendidikan Matematika) 7, no. 1 (2018): 22. http://dx.doi.org/10.25273/jipm.v7i1.2123.

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The ability to solve mathematical problems is very important in learning math and everyday life. According to Krulik & Rudnick there are 5 stages of problem solving that is Read, Explore, Select A Strategy, Solve And Look Back. Mathematical problems require multiple representational skills to communicate problems, one of which is visual representation. Trigonometry is one of the materials that uses visual representation. This research is a qualitative descriptive research that aims to describe the ability of problem solving mathematics with Krulik & Rudnick stages in terms of visual representation. The study was conducted in MAN 2 Brebes. Determination of Subjects in this study using Purposive Sampling. Research instruments used to obtain the required data are visual representation and problem-solving tests, and interview guidelines. The data obtained were analyzed based on the Krulik & Rudnick problem solving indicator. Subjects in this study were subjects with moderate visual representation. Based on the results, problem solving ability of the subject is not fully fulfilled. Subjects with visual representations are able to do problem solving well that is solving the problem through a concept that is understood without visualization of the image. Subjects with visual representations are having a schematic visual representation type.
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Stone, Christopher, and Larry Bull. "For Real! XCS with Continuous-Valued Inputs." Evolutionary Computation 11, no. 3 (2003): 299–336. http://dx.doi.org/10.1162/106365603322365315.

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Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other Learning Classifier System architectures. We discuss limitations of the real multiplexer problem, a benchmark problem used for Learning Classifier Systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer. Representations and operators are compared using both the real multiplexer and checkerboard problems and we find that representational, operator and sampling bias all affect the performance of XCS in continuous-valued environments.
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Rextin, Aimal Tariq, Fatima Iftekhar, Muhammad Muddasir Malik, and Faheem Abbas. "Comparative Analysis of Flight Search Representations." Journal of Organizational and End User Computing 31, no. 4 (2019): 74–88. http://dx.doi.org/10.4018/joeuc.2019100104.

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Visualization has been extensively used in almost all the fields, but unfortunately, there is very little work on the visualization of flight search. The literature discusses only two representations: textual representation and linear representation. In this study, the authors introduce a new form of visualization called circular representation and compare it with the existing visualization techniques. The authors found that each representation has its own merits and demerits. More specifically, they found that users were able to find required flight circular efficiently when using circular representation, while most errors were committed by the user when using textual representation, and users ranked circular representation low in terms of qualitative satisfaction.
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Kawabata, Ryo, Yuki Ishikawa, Kenji Masuda, and Kiyoshi Itoh. "THE INTEGRATED ENVIRONMENT FOR SUPPORTING COLLABORATIVE ANALYSIS." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 12, no. 2 (2008): 11–26. http://dx.doi.org/10.3233/jid-2008-12203.

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In systems analysis, the target system is analyzed by collaboration between two or more analysts. Systems development is performed by analyzing the target system from various viewpoints. From each viewpoint, corresponding chart representation which is suitable for the viewpoint is described. The chart representation is not described from scratch, but it is described by using knowledge included in the other chart representations that are already described. There are two types of collaborations in describing chart representations. The one type is that plural analysts describe the one chart representation collaboratively. The other type is that each analyst describes different kinds of chart representations. In this paper, the authors propose the system called CiCCS (Collaborative inter-Chart Conversion System) for supporting to describe chart representations by collaboration between two or more analysts. CiCCS has analysis processes which include a set of chart representations, the usage of them and the order of them. The navigator guides system analysts with the analysis processes which include a set of chart representations and the usage of them according to the purpose of analysis. Analysts can describe chart representations in accordance with the analysis process or analyst's own order. CiCCS has a converter which extracts a skeleton of a chart representation from the other chart representations.
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ABALASEI, BEATRICE-AURELIA. "Sport games: An analysis of representation." New Trends and Issues Proceedings on Humanities and Social Sciences 4, no. 1 (2017): 164–68. http://dx.doi.org/10.18844/prosoc.v4i1.2250.

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Khaerun Nisa, Rachmawati, and Reza Muhamad Zaenal. "Analysis Of Students' Mathematical Representation Ability in View of Learning Styles." Indo-MathEdu Intellectuals Journal 4, no. 2 (2023): 99–109. http://dx.doi.org/10.54373/imeij.v4i2.119.

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Students' mathematical representation ability from the point of view of learning styles, we can see that learning styles play an important role in how students absorb mathematical information. The visual learning style involves using pictures, diagrams and other visual representations to understand math concepts. The auditory learning style involves a preference for listening to oral explanations or participating in discussions. Meanwhile, the kinesthetic learning style involves physical movement to gain an understanding of mathematics. This study aims to determine: 1) the representational ability of students who have a visual learning style, 2) the representational ability of students who have a kinesthetic learning style, 3) the representational ability of students who have an auditory learning style. In this study using descriptive qualitative research methods to determine the ability of mathematical representation which has a learning style for each student in class VII-F SMP Negeri 2 Kuningan. Data were collected through learning style questionnaires, representation ability tests and interviews with students. The results of the learning style questionnaire for the majority of students have a rich visual learning of 42%, kinesthetic 30% and auditory 28%. The results of the representation ability test showed that students with visual learning styles had high mathematical representation abilities, while the majority of students with auditory and kinesthetic learning styles were categorized as moderate. Interviews with students revealed that learning understanding can be understood by means of their learning styles. However, teachers face obstacles in choosing learning models that suit students' learning styles and need to understand individual learning needs. Students' activities in learning mathematics also still need to be improved, especially in the ability to discuss, conclude, and make summaries. Therefore, further efforts are needed to pay attention to differences in student abilities and develop appropriate strategies to increase student interaction and participation in learning mathematics.
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Zhang, Jun. "Divergence Function, Duality, and Convex Analysis." Neural Computation 16, no. 1 (2004): 159–95. http://dx.doi.org/10.1162/08997660460734047.

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From a smooth, strictly convex function Φ: Rn → R, a parametric family of divergence function DΦ(α) may be introduced: [Formula: see text] for x, y, ε int dom(Φ) and for α ε R, with DΦ(±1 defined through taking the limit of α. Each member is shown to induce an α-independent Riemannian metric, as well as a pair of dual α-connections, which are generally nonflat, except for α = ±1. In the latter case, D(±1)Φ reduces to the (nonparametric) Bregman divergence, which is representable using and its convex conjugate Φ * and becomes the canonical divergence for dually flat spaces (Amari, 1982, 1985; Amari & Nagaoka, 2000). This formulation based on convex analysis naturally extends the information-geometric interpretation of divergence functions (Eguchi, 1983) to allow the distinction between two different kinds of duality: referential duality (α -α) and representational duality (Φ  Φ *). When applied to (not necessarily normalized) probability densities, the concept of conjugated representations of densities is introduced, so that ± α-connections defined on probability densities embody both referential and representational duality and are hence themselves bidual. When restricted to a finite-dimensional affine submanifold, the natural parameters of a certain representation of densities and the expectation parameters under its conjugate representation form biorthogonal coordinates. The alpha representation (indexed by β now, β ε [−1, 1]) is shown to be the only measure-invariant representation. The resulting two-parameter family of divergence functionals D(α, β), (α, β) ε [−1, 1] × [-1, 1] induces identical Fisher information but bidual alpha-connection pairs; it reduces in form to Amari's alpha-divergence family when α =±1 or when β = 1, but to the family of Jensen difference (Rao, 1987) when β = 1.
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Erita, Selvia, Tami Mulyani, and Aan Putra. "Analysis Of Mathematic Representation Ability In Online Learning." Mathline : Jurnal Matematika dan Pendidikan Matematika 8, no. 1 (2023): 101–12. http://dx.doi.org/10.31943/mathline.v8i1.259.

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ABSTRACT The outbreak of the corona virus has had an impact on various sectors, especially the education sector. The application of online learning has been implemented in almost all schools in Indonesia. However, judging from the results of various studies and some experiences, the online learning process in Indonesia has previously been carried out well. Mathematical representation skills are needed so that students understand mathematical concepts well. This study aims to analyze the ability of mathematical representation in online learning. The method used is descriptive qualitative type. The instrument used is a mathematical representation ability test and interview guidelines. The data analysis technique used is the stages of data reduction, data display and making overall conclusions. Data analysis of test results was used to determine the level of students' mathematical representation abilities, while interviews were to strengthen test results and determine student learning constraints. Based on the results of the study, it was found that students' visual representations were more dominant than other types of representations. The students' mathematical visual representation ability is in the sufficient (moderate) category, while the students' symbolic representation ability is very poor and the students' verbal representation ability is in the poor category.
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Dissertations / Theses on the topic "Representation analysis"

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Kachintseva, Dina (Dina D. ). "Semantic knowledge representation and analysis." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/76983.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 103).<br>Natural language is the means through which humans convey meaning to each other - each word or phrase is a label, or name, for an internal representation of a concept. This internal representation is built up from repeated exposure to particular examples, or instances, of a concept. The way in which we learn that a particular entity in our environment is a "bird" comes from seeing countless examples of different kinds of birds. and combining these experiences to form a menial representation of the concept. Consequently, each individual's understanding of a concept is slightly different, depending on their experiences. A person living in a place where the predominant types of birds are ostriches and emus will have a different representation birds than a person who predominantly sees penguins, even if the two people speak the same language. This thesis presents a semantic knowledge representation that incorporates this fuzziness and context-dependence of concepts. In particular, this thesis provides several algorithms for learning the meaning behind text by using a dataset of experiences to build up an internal representation of the underlying concepts. Furthermore, several methods are proposed for learning new concepts by discovering patterns in the dataset and using them to compile representations for unnamed ideas. Essentially, these methods learn new concepts without knowing the particular label - or word - used to refer to them. Words are not the only way in which experiences can be described - numbers can often communicate a situation more precisely than words. In fact, many qualitative concepts can be characterized using a set of numeric values. For instance, the qualitative concepts of "young" or "strong" can be characterized using a range of ages or strengths that are equally context-specific and fuzzy. A young adult corresponds to a different range of ages from a young child or a young puppy. By examining the sorts of numeric values that are associated with a particular word in a given context, a person can build up an understanding of the concept. This thesis presents algorithms that use a combination of qualitative and numeric data to learn the meanings of concepts. Ultimately, this thesis demonstrates that this combination of qualitative and quantitative data enables more accurate and precise learning of concepts.<br>by Dina Kachintseva.<br>M.Eng.
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Ji, Donghong. "Conceptual relevance : representation and analysis." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711639.

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Wilhelmson, Mika. "Representations of culture in EIL : Cultural representation in Swedish EFL textbooks." Thesis, Högskolan Dalarna, Engelska, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:du-21120.

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The English language has become an international language and is globally used as a lingua franca. Therefore, there has been a shift in English-language education toward teaching English as an interna-tional language (EIL). Teaching from the EIL paradigm means that English is seen as an international language used in communication by people from different linguistic and cultural backgrounds. As the approach to English-language education changes from the traditional native-speaker, target country context, so does the role of culture within English-language teaching. The aim of this thesis is to in-vestigate and analyse cultural representations in two Swedish EFL textbooks used in upper-secondary school to see how they correspond with the EIL paradigm. This is done by focusing on the geograph-ical origin of the cultural content as well as looking at what kinds of culture are represented in the textbooks. A content analysis of the textbooks is conducted, using Kachru’s Concentric Circles of English as the model for the analysis of the geographical origin. Horibe’s model of the three different kinds of culture in EIL is the model used for coding the second part of the analysis. The results of the analysis show that culture of target countries and "Culture as social custom" dominate the cultural content of the textbook. Thus, although there are some indications that the EIL paradigm has influ-enced the textbooks, the traditional approach to culture in language teaching still prevails in the ana-lysed textbooks. Because of the relatively small sample included in the thesis, further studies need to be conducted in order to make conclusions regarding the Swedish context as a whole.
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Sanchez, Carmona Vicente Ivan. "Experimental analysis of representation learning systems." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10056389/.

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Studying a subject is central to understanding its behavior and what it has learned. In this thesis, we study specific aspects of five representation learning systems for natural language processing tasks. Representation learning systems are a type of machine learning system dedicated to learn representations of data suitable for other machine learning systems, such as classifiers, to operate upon them. Thus, understanding the behavior of and the abilities learned by representation learning systems is crucial for improving the results on the tasks they are used. The aspects on which we focus are interpretability, robustness, and abilities learned. We are interested in obtaining explanations that allow us to understand how a system makes a decision, what factors from the data and internal to the system affect its robustness, and to what extent it has learned a linguistic ability. To do so, we propose to carry out three types of analyses, namely functional, behavioral, and internal analyses which we link with work on the cognitive science, behavioral science, and neuroscience. We present three case studies. In the first study, we provide a functional explanation of a matrix factorization system that allow us to understand how this system makes a prediction. In our second study, we investigate how robust are three systems when the input data suffers a simple transformation and how certain external and internal factors influence their behavior; these systems are trained for the task of natural language inference. Finally, our third study shows that we are able to extract hypernymy from the word embeddings of a popular ReLe system, while studying the influence that the choice of hypernymy dataset plays in the task. In summary, we advance towards better understanding ReLe systems by providing explanations of their predictive behavior and investigating abilities learned by these systems.
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Shen, Guoling 1967. "Analysis of boundary representation model rectification." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/88491.

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Altaf, Muhammad Umair Bin. "Environmental sounds: acquisition, analysis, and representation." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53953.

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The dissertation presents the design and development of a systematic signal analysis and representation framework beyond short-time Fourier power spectrum for sounds, in particular environmental sounds. This framework is consistent with the underlying assumptions of the analysis method and its elements are correlated with human perception. The sound signal has to conform to certain conditions for its power spectrum to have a physical and perceptual meaning. We contend that very few environmental sounds readily meet these criteria and argue that the quantities that are traditionally used to describe sounds need to be repurposed and, if necessary, redefined to represent sounds by non-Fourier means. We propose a perceptuo-analytic organization of sounds so that any environmental sound can be analyzed based on its signal characteristics and perception. We present environmental sound acquisition in the context of collection and annotation of a database for the footstep sounds, a common environmental sound, and show that it can be represented by these unconventional means and further analyzed to produce descriptions which are obscured with the traditional analysis. We present a novel application of extracting gait characteristics from the footstep sounds which is enabled by the proposed framework.
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Albrecht, Birgit. "Novel representation and analysis of protein structure." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418641.

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Shum, Simon J. "A cognitive analysis of design rationale representation." Thesis, University of York, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306290.

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Simoncelli, Eero Peter. "Distributed representation and analysis of visual motion." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12590.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.<br>Includes bibliographical references (leaves 171-179).<br>by Eero Peter Simoncelli.<br>Ph.D.
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Mukras, Rahman. "Representation and learning schemes for sentiment analysis." Thesis, Robert Gordon University, 2009. http://hdl.handle.net/10059/379.

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This thesis identifies four novel techniques of improving the performance of sentiment analysis of text systems. Thes include feature extraction and selection, enrichment of the document representation and exploitation of the ordinal structure of rating classes. The techniques were evaluated on four sentiment-rich corpora, using two well-known classifiers: Support Vector Machines and Na¨ıve Bayes. This thesis proposes the Part-of-Speech Pattern Selector (PPS), which is a novel technique for automatically selecting Part-of-Speech (PoS) patterns. The PPS selects its patterns from a background dataset by use of a number of measures including Document Frequency, Information Gain, and the Chi-Squared Score. Extensive empirical results show that these patterns perform just as well as the manually selected ones. This has important implications in terms of both the cost and the time spent in manual pattern construction. The position of a phrase within a document is shown to have an influence on its sentiment orientation, and that document classification performance can be improved by weighting phrases in this regard. It is, however, also shown to be necessary to sample the distribution of sentiment rich phrases within documents of a given domain prior to adopting a phrase weighting criteria. A key factor in choosing a classifier for an Ordinal Sentiment Classification (OSC) problem is its ability to address ordinal inter-class similarities. Two types of classifiers are investigated: Those that can inherently solve multi-class problems, and those that decompose a multi-class problem into a sequence of binary problems. Empirical results showed the former to be more effective with regard to both mean squared error and classification time performances. Important features in an OSC problem are shown to distribute themselves across similar classes. Most feature selection techniques are ignorant of inter-class similarities and hence easily overlook such features. The Ordinal Smoothing Procedure (OSP), which augments inter-class similarities into the feature selection process, is introduced in this thesis. Empirical results show the OSP to have a positive effect on mean squared error performance.
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Books on the topic "Representation analysis"

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Ton-That, Tuong, Kenneth I. Gross, Donald St P. Richards, and Paul J. Sally, eds. Representation Theory and Harmonic Analysis. American Mathematical Society, 1995. http://dx.doi.org/10.1090/conm/191.

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Mahadevan, Sridhar. Representation Discovery using Harmonic Analysis. Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01546-5.

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Cowling, Michael, Edward Frenkel, Masaki Kashiwara, Alain Valette, David A. Vogan, and Nolan R. Wallach. Representation Theory and Complex Analysis. Edited by Enrico Casadio Tarabusi, Andrea D'Agnolo, and Massimo Picardello. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76892-0.

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Talamanca, A. Figà, ed. Harmonic Analysis and Group Representation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11117-4.

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Mahadevan, Sridhar. Representation discovery using harmonic analysis. Morgan & Claypool Publishers, 2008.

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Alaska. Division of Juvenile Justice., ed. Analysis of minority youth representation. The Division, 2001.

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service), SpringerLink (Online, ed. Harmonic Analysis and Group Representation. Springer-Verlag Berlin Heidelberg, 2011.

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Burge, Mark. Representation and analysis of document images. Österreichische Computer Gesellschaft, 1999.

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Conway, Joseph B. Analysis and representation of fatigue data. ASM International, 1991.

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Jane, Oakhill, and Garnham Alan 1954-, eds. Discourse representation and text processing. Lawrence Erlbaum, 1992.

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Book chapters on the topic "Representation analysis"

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Lacey, Nick. "Advanced Textual Analysis." In Image and Representation. Macmillan Education UK, 2009. http://dx.doi.org/10.1007/978-1-137-28800-4_5.

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Lacey, Nick. "Advanced Image Analysis." In Image and Representation. Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-26712-5_5.

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Ganter, Bernhard, and Rudolf Wille. "Determination and Representation." In Formal Concept Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59830-2_3.

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Aliprantis, Charalambos D., and Kim C. Border. "Riesz Representation Theorems." In Infinite Dimensional Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03961-8_13.

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Ganter, Bernhard, and Rudolf Wille. "Determination and representation." In Formal Concept Analysis. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63422-2_3.

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Richards, Keith. "Analysis and Representation." In Qualitative Inquiry in TESOL. Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230505056_6.

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Resnikoff, Howard L., and Raymond O. Wells. "Multiscale Representation of Geometry." In Wavelet Analysis. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0593-7_11.

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Lanza, Claudia, Abdelkader Lahmadi, and Jérôme François. "Classification and Knowledge Representation." In Ransomware Analysis. CRC Press, 2024. http://dx.doi.org/10.1201/9781003528999-1.

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Cock, Barbara De, Philippe Hambye, and Andrea Pizarro Pedraza. "Annotation and mark up for representation analysis." In Analysing Representation. Routledge, 2024. http://dx.doi.org/10.4324/9781003350972-6.

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Yarlagadda, R. K. Rao, and John E. Hershey. "Signal Representation." In Hadamard Matrix Analysis and Synthesis. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6313-6_20.

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Conference papers on the topic "Representation analysis"

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Shao, Zhong. "Flexible representation analysis." In the second ACM SIGPLAN international conference. ACM Press, 1997. http://dx.doi.org/10.1145/258948.258958.

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Simoncelli, Eero. "Nonlinear Image Representation: Lessons from Biology." In Digital Image Processing and Analysis. OSA, 2010. http://dx.doi.org/10.1364/dipa.2010.dmb1.

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Sylcott, Brian, Seth Orsborn, and Jonathan Cagan. "The Effect of Product Representation in Visual Conjoint Analysis." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34443.

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When most designers set out to develop a new product they solicit feedback from potential consumers. These data are incorporated into the design process in an effort to more effectively meet customer requirements. Often these data are used to construct a model of consumer preference capable of evaluating candidate designs. Although the mechanics of these models have been extensively studied there are still some open questions, particularly with respect to models of aesthetic preference. When constructing preference models, simplistic product representations are often favored over high fidelity product models in order to save time and expense. This work investigates how choice of product representation can affect model performance in visual conjoint analysis. Preference models for a single product, a table knife, are derived using three different representation schemes; simple sketches, solid models, and 3D printed models. Each of these representations is used in a separate conjoint analysis survey. The results from this study showed that consumer responses were inconsistent and potentially contradictory between different representations. Consequently, when using conjoint analysis for product innovation, obtaining a true understanding of consumer preference requires selecting representations based on how accurately they convey the product details in question.
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Strout, Michelle Mills, John Mellor-Crummey, and Paul Hovland. "Representation-independent program analysis." In The 6th ACM SIGPLAN-SIGSOFT workshop. ACM Press, 2005. http://dx.doi.org/10.1145/1108792.1108810.

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Eelbode, David, Peter Van Lancker, and Vladimír Souček. "The Fueter theorem by representation theory." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756132.

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Özay, Evrim Korkmaz, Metin Demiralp, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Piecewise Vector High Dimensional Model Representation." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637818.

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Martini, Kirk. "Ancient Structures and Modern Analysis: Investigating Damage and Reconstruction at Pompeii." In ACADIA 1997: Representation and Design. ACADIA, 1997. http://dx.doi.org/10.52842/conf.acadia.1997.283.

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Şen, Erdòan, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "On Quartic Transformational High Dimensional Model Representation." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3640041.

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Falzon, Lucia, Caitlin McCurrie, and John Dunn. "Representation and Analysis of Twitter Activity." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. ACM, 2017. http://dx.doi.org/10.1145/3110025.3122118.

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Herunata, Herunata, Shofia Dwi Kusumawati, Husni Wahyu Wijaya, and Parlan Parlan. "Correlational analysis conceptual understanding, chemical representation, and representational competence on stoichiometry." In THE 5TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCE EDUCATION (ICoMSE) 2021: Science and Mathematics Education Research: Current Challenges and Opportunities. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0112179.

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Reports on the topic "Representation analysis"

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Schunn, Christian D., Lelyn D. Saner, Susan K. Kirschenbaum, J. G. Trafton, and Eliza B. Littleton. Complex Visual Data Analysis, Uncertainty, and Representation. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada479656.

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Fink, Eugene, Jaime G. Carbonell, Anatole Gershman, Ganesh Mani, and Dwight Dietrich. Representation and Analysis of Probabilities Intelligence Data (RAPID). Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada498238.

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Karamchetty, Som. Natural Computing: Analysis of Tables for Computer Representation. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada374338.

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Karamchetty, Som. Natural Computing: Analysis of Graphs for Computer Representation. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada375557.

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Krýsl, Svatopluk. Analysis Over $C^*$-Algebras and the Oscillatory Representation. GIQ, 2014. http://dx.doi.org/10.7546/giq-15-2014-173-195.

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Krysl, Svatopluk Krysl. Analysis Over $C^*$-Algebras and the Oscillatory Representation. Jgsp, 2014. http://dx.doi.org/10.7546/jgsp-33-2014-1-25.

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Ahrens, Cory, Gregory Beylkin, and Kristian Sandberg. Geospatial Representation, Analysis and Computing Using Bandlimited Functions. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada565379.

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Johnson, Jay Dean, Jon Craig Helton, William Louis Oberkampf, and Cedric J. Sallaberry. Representation of analysis results involving aleatory and epistemic uncertainty. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/940535.

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Spector, Lee, Bill Andersen, James Hendler, et al. Knowledge Representation in PARKA. Part 2. Experiments, Analysis, and Enhancements. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada454920.

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Allam, M., and G. Plundett. Systems for data processing, analysis and representation: ISPRS commission II symposium. Natural Resources Canada/CMSS/Information Management, 1994. http://dx.doi.org/10.4095/331151.

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