Relevant bibliographies by topics / Discrimination learning

Academic literature on the topic 'Discrimination learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Discrimination learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Discrimination learning"

1

Trobalon, J. B., J. Sansa, V. D. Chamizo, and N. J. Mackintos. "Perceptual Learning in Maze Discriminations." Quarterly Journal of Experimental Psychology Section B 43, no. 4b (November 1991): 389–402. http://dx.doi.org/10.1080/14640749108401276.

Full text
Abstract:
In Experiment 1, rats were trained on a discrimination between rubber- and sandpaper-covered arms of a maze after one group had been pre-exposed to these intra-maze cues. Pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made easier by adding further discriminative stimuli, when it now significantly retarded learning. In Experiment 2, rats were trained on an extra-maze spatial discrimination, again after one group, but not another, had been pre-exposed to the extra-maze landmarks. Here too, pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made substantially easier by arranging that the two arms between which rats had to choose were always separated by 135°. The results of both experiments can be explained by supposing that perceptual learning depends on the presence of features common to S+ and S-.
APA, Harvard, Vancouver, ISO, and other styles
2

Pérez-González, Luis Antonio, and Héctor Martínez. "Emergence of Third-Order Conditional Discriminations from Learning Discriminations with Unrelated Stimuli." Psychological Record 72, no. 1 (November 17, 2021): 75–88. http://dx.doi.org/10.1007/s40732-021-00461-2.

Full text
Abstract:
AbstractThis study explored learning and generalization of a third-order conditional discrimination. Two 8-year-old children learned two auditory–visual conditional discriminations in which they selected visual Japanese syllabic symbols in response to syllables spoken by the experimenter. Then, they learned a third-order conditional discrimination in which they selected between two visual symbols after being exposed to two spoken syllables and one visual symbol. Thereafter, we probed generalization with novel symbols and names by teaching two additional conditional discriminations with Nahuatl symbols and spoken words and probing without reinforcement a new third-order conditional discrimination in which they had to select between two visual Nahuatl symbols after being exposed to two spoken Nahuatl words and one visual Nahuatl symbol. The two children responded in a predicted way to the novel third-order conditional discrimination. The emergent performance was possible because the set of relations established among the stimuli of the third-order conditional discrimination with Japanese syllables was analogous to the set of relations established among the stimuli of the third-order conditional discriminations with Nahuatl words. These results demonstrated a novel type of emergent responding in third-order conditional discrimination with arbitrary relations.
APA, Harvard, Vancouver, ISO, and other styles
3

Keith, Kenneth D. "Peak Shift Phenomenon: A Teaching Activity for Basic Learning Theory." Teaching of Psychology 29, no. 4 (October 2002): 298–300. http://dx.doi.org/10.1207/s15328023top2904_09.

Full text
Abstract:
Stimulus discrimination is a standard subject in undergraduate courses presenting basic principles of learning, and a particularly interesting aspect of discrimination is the peak shift phenomenon. Peak shift occurs in generalization tests following intradimensional discrimination training as a displacement of peak responding away from the S+ (a stimulus signaling availability of reinforcement) in a direction opposite the S– (a stimulus signaling lack of reinforcement). This activity allows students to develop intradimensional discriminations that enable firsthand observation of the peak shift phenomenon. Evaluation of the activity suggests that it produces improved understanding of peak shift and that undergraduate students can demonstrate peak shift in simple discrimination tasks.
APA, Harvard, Vancouver, ISO, and other styles
4

Zheng, Hao, and Dapeng Tao. "Discriminative dictionary learning via Fisher discrimination K-SVD algorithm." Neurocomputing 162 (August 2015): 9–15. http://dx.doi.org/10.1016/j.neucom.2015.03.071.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Byrom, Nicola C., and Robin A. Murphy. "Cue competition influences biconditional discrimination." Quarterly Journal of Experimental Psychology 72, no. 2 (January 1, 2018): 182–92. http://dx.doi.org/10.1080/17470218.2017.1363256.

Full text
Abstract:
When multiple cues are presented in compound and trained to predict an outcome, the cues may compete for association with an outcome. However, if both cues are necessary for solution of the discrimination, then competition might be expected to interfere with the solution of the discrimination. We consider how unequal stimulus salience influences learning in configural discriminations, where no individual stimulus predicts the outcome. We compared two hypotheses: (1) salience modulation minimises the initial imbalance in salience and (2) unequal stimulus salience will impair acquisition of configural discriminations. We assessed the effect of varying stimulus salience in a biconditional discrimination (AX+, AY−, BX−, BY+). Across two experiments, we found stronger discrimination when stimuli had matched, rather than mismatched, salience, supporting our second hypothesis. We discuss the implications of this finding for Mackintosh’s model of selective attention, modified elemental models and configural models of learning.
APA, Harvard, Vancouver, ISO, and other styles
6

Ren, Qiubing, Mingchao Li, Shuai Han, Ye Zhang, Qi Zhang, and Jonathan Shi. "Basalt Tectonic Discrimination Using Combined Machine Learning Approach." Minerals 9, no. 6 (June 22, 2019): 376. http://dx.doi.org/10.3390/min9060376.

Full text
Abstract:
Geochemical discrimination of basaltic magmatism from different tectonic settings remains an essential part of recognizing the magma generation process within the Earth’s mantle. Discriminating among mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB) is that matters to geologists because they are the three most concerned basalts. Being a supplement to conventional discrimination diagrams, we attempt to utilize the machine learning algorithm (MLA) for basalt tectonic discrimination. A combined MLA termed swarm optimized neural fuzzy inference system (SONFIS) was presented based on neural fuzzy inference system and particle swarm optimization. Two geochemical datasets of basalts from GEOROC and PetDB served as to test the classification performance of SONFIS. Several typical discrimination diagrams and well-established MLAs were also used for performance comparisons with SONFIS. Results indicated that the classification accuracy of SONFIS for MORB, OIB and IAB in both datasets could reach over 90%, superior to other methods. It also turns out that MLAs had certain advantages in making full use of geochemical characteristics and dealing with datasets containing missing data. Therefore, MLAs provide new research tools other than discrimination diagrams for geologists, and the MLA-based technique is worth extending to tectonic discrimination of other volcanic rocks.
APA, Harvard, Vancouver, ISO, and other styles
7

Maddess, T., D. Coy, J. C. Herrington, C. F. Carle, F. Sabeti, and M. S. Barbosa. "Learning complex texture discrimination." Journal of the Optical Society of America A 38, no. 3 (March 1, 2021): 449. http://dx.doi.org/10.1364/josaa.413065.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Blume, Lawrence E. "Learning and Statistical Discrimination." American Economic Review 95, no. 2 (April 1, 2005): 118–21. http://dx.doi.org/10.1257/000282805774670257.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Herrington, Jessica, Ted Maddess, Dominique Coy, Corinne Carle, Faran Sabeti, and Marconi Barbosa. "Learning Complex Texture Discrimination." Journal of Vision 18, no. 10 (September 1, 2018): 260. http://dx.doi.org/10.1167/18.10.260.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jain, A. K., and K. Karu. "Learning texture discrimination masks." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 2 (1996): 195–205. http://dx.doi.org/10.1109/34.481543.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Discrimination learning"

1

Livesey, Evan James. "Discrimination learning and stimulus representation." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614066.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Devalle, D. A. "Discrimination without awareness." Thesis, Bangor University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382758.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hyatt, Charles Winton. "Discrimination learning in the African elephant." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/28887.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Walker, Jacqueline G. "Auditory discrimination learning with developmentally disabled persons." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0005/NQ41630.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wallace, Benjamin E. "ESSAYS ON PRICE DISCRIMINATION AND DEMAND LEARNING." UKnowledge, 2019. https://uknowledge.uky.edu/economics_etds/40.

Full text
Abstract:
This dissertation consists of three essays examining how and why firms set prices in markets. In particular, this dissertation shows how firms may utilize nonlinear pricing to price discriminate, how firms may experiment with the prices they set to learn about the demand function in the market they serve in later periods and the effects of these pricing strategies on consumer welfare. In Essay 1, I show how firms in the milk market use nonlinear price schedules -- quantity discounts -- to price discriminate and increase profits. I find that firms have a greater ability to price discriminate on their own ``private label'' products rather than regional branded that they sell alongside their own. Though some consumers benefit from a lower price as a result of the price discrimination, total consumer surplus is lower than if the store had to offer a fixed price per unit. Additionally, I compare my structural demand estimates, which using the Nielsen household panel data include consumer demographic information and actual household choices, to the standard approach in the literature on price discrimination that uses only market level data. By doing so I find that ignoring demographic information and actual consumer choices leads to biased parameter estimates. In the case of the milk market, the biased parameter estimates due to ignoring household demographic information and actual consumer choices lead to underestimating welfare harm to consumers on average. After finding that price discrimination harms consumers overall in this market, I quantify which consumer demographic are better off and which are worse off. I find that households with children and low income households with children are the only households to benefit from the price discriminatory practices of firms in this market. Since these groups are particularly vulnerable, I suggest that policymakers take no action to correct this market, as any action will directly hurt these consumer groups. In Essay 2, I study how firms learn about the demand in a new market by exploiting a significant change in Washington's state's liquor laws. In 2012, the state of Washington switched from a price-controlled state-store system of selling liquor to one in which private sellers could sell liquor with minimal restrictions on price and range of products. As a result, a heterogeneous group of firms entered the liquor market across the state with little knowledge of the regional demand for alcohol in the state of Washington across heterogeneous localities. Using the Nielsen retail scanner data I am able to observe the variation in pricing and offerings seasonally and over time to see if there is convergence in offerings and prices, and how quickly that convergence occurs across different localities depending on local demographics and competition. I also investigate the extent to which the variation is "experimentation'' by the firms, i.e., the firms purposely experimenting to learn more about demand and the extent that local demographics and competition can affect the experimentation and whether there are spill-overs from local competition (i.e. do firms learn from each other and does this effect how much they experiment and how quickly they learn). My main findings are that over time, firms within this market have learned better how to price discriminate over the holiday season; firms experiment more with prices for the pint sized products than the larger sizes; and that menu of options that firms have offered has been expanding but at a slower rate, suggesting that they are approaching a long-run steady state for the optimal menu of options.
APA, Harvard, Vancouver, ISO, and other styles
6

Zhu, Beibei. "Three Essays on Employer Learning and Statistical Discrimination." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23168.

Full text
Abstract:
This dissertation consists of three essays studying employer learning and statistical discrimination of young workers in the U.S. labor market. The first chapter outlines the dissertation by discussing the motivations, methods, and research findings.

Chapter two develops a framework that nests both symmetric and asymmetric employer learning, and derives testable hypotheses on racial statistical discrimination under different processes of employer learning. Testing the model with data from the NLSY79, we find that employers statistically discriminate against black workers on the basis of both education and race in the high school market where learning appears to be mostly asymmetric. In the college market, employers directly observe most parts of the productivity of potential employees and learn very little over time.

In chapter three, we investigate how the process of employer learning and statistical discrimination varies over time and across employers. The comparison between the NLSY79 and the NLSY97 cohorts reveals that employer learning and statistical discrimination has became stronger over the past decades. Using the NLSY97 data, we identify three employer- specific characteristics that influencing employer learning and statistical discrimination, the supervisor-worker race match, supervisor\'s age, and firm size. Black high school graduates face weaker employer learning and statistical discrimination if they choose to work for a black supervisor, work for an old supervisor, or work in a firm of small size.

In the last chapter, we are interested in the associations between verbal and quantitative skills and individual earnings as well as the employer learning process of these two specific types of skills. There exist significant differences in both the labor market rewards and employer learning process of verbal and quantitative skills between high school and college graduates. Verbal skills are more important than quantitative skills for high school graduates, whereas college-educated workers benefit greatly from having high quantitative skills but little from having high verbal skills. In addition, employers directly learn verbal skills and continuously learn quantitative skills in the high school market, but almost perfectly observe quantitative skills in the college market.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
7

Honey, R. "Conditioning and discrimination after nonreinforced stimulus preexposure." Thesis, University of York, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378062.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lavis, Yvonna Marie Psychology Faculty of Science UNSW. "An investigation of the mechanisms responsible for perceptual learning in humans." Publisher:University of New South Wales. Psychology, 2008. http://handle.unsw.edu.au/1959.4/42882.

Full text
Abstract:
Discrimination between similar stimuli is enhanced more by intermixed pre-exposure than by blocked pre-exposure to those stimuli. The salience modulation account of this intermixed-blocked effect proposes that the unique elements of intermixed stimuli are more salient than those of blocked stimuli. The inhibition account proposes that inhibitory links between the unique elements of intermixed stimuli enhance discrimination. The current thesis evaluated the two accounts in their ability to explain this effect in humans. In Experiments 1 and 2, categorisation and same-different judgements were more accurate for intermixed than for blocked stimuli. This indicates that intermixed pre-exposure decreases generalisation and increases discriminability more than does blocked pre-exposure. In Experiments 3 ?? 5, same-different judgements were more accurate when at least one of the two stimuli was intermixed. This enhanced discrimination was not confined to two stimuli that had been directly intermixed. These results are better explained by salience modulation than by inhibition. Experiments 6 ?? 8 employed dot probe tasks, in which a grid stimulus was followed immediately by a probe. Neither intermixed nor blocked stimuli showed facilitated reaction times when the probe appeared in the location of the unique element. In Experiments 9 ?? 11 participants learned to categorise the intermixed unique elements more successfully than the blocked unique elements, but only when the unique elements were presented on a novel background during categorisation. Experiments 6 ?? 11 provide weak evidence that the intermixed unique elements are more salient than their blocked counterparts. In Experiment 12, participants were presented with the shape and location of a given unique element, and were required to select the correct colour. Performance was more accurate for intermixed than for blocked unique elements. In Experiment 13, participants learned to categorise intermixed, blocked and novel unique elements. Performance was better for intermixed than for blocked and novel unique elements, which did not differ. None of the proposed mechanisms for salience modulation anticipate these results. The intermixed-blocked effect in human perceptual learning is better explained by salience modulation than by inhibition. However, the salience modulation accounts that have been proposed received little support. An alternative account of salience modulation is considered.
APA, Harvard, Vancouver, ISO, and other styles
9

Sentís, Herrera Gael. "Dealing with ignorance: universal discrimination, learning and quantum correlations." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/134830.

Full text
Abstract:
Una de las tareas fundamentales de la Teor a de la Informaci on Cu antica consiste en identi car el estado en que ha sido preparado un sistema cu antico. Cuando las posibles preparaciones forman una lista nita de estados, el problema recibe el nombre de discriminaci on de estados. El caso fundamental de unicamente dos estados posibles se conoce tambi en bajo el nombre de contraste de hip otesis. Una de las caracter sticas distintivas de la teor a cu antica es el hecho de que para dos estados no ortogonales no hay medida concebible que pueda identi car el estado del sistema con certeza. El indeterminismo cu antico exige, por tanto, un enfoque probabil stico para llevar a cabo esta tarea. Habitualmente se considera que los estados posibles del sistema son conocidos para el experimentador. En esta tesis analizo el papel que desempe~ na la informaci on previa disponible en la tarea de discriminaci on y, en particular, analizo situaciones en las que dicha informaci on es incompleta. Suponiendo una total ignorancia de la identidad de los estados posibles, estudio la probabilidad de error de una m aquina programable de discriminaci on para estados de qubit. Esta m aquina incorpora la informaci on sobre los estados en forma de programas de entrada donde se introducen los sistemas cu anticos en las diferentes preparaciones. Es decir, la informaci on es utilizada en su forma genuinamente cu antica, en lugar de como una descripci on cl asica de los estados. Esta ignorancia cl asica se tiene en cuenta en el dise~no de la m aquina, la cual ya no es espec ca para cada caso, sino que es capaz de discriminar entre cualquier par de estados de qubit, una vez ha sido convenientemente programada mediante las entradas de estados cu anticos. Estudio en detalle el rendimiento optimo de estas m aquinas para estados de qubit generales cuando se dispone de un n umero de copias arbitrario, tanto de los programas como del estado que se ha de identi car. Espec camente, obtengo las probabilidades de correcta identi caci on en los esquemas usuales de error m nimo y discriminaci on no ambigua, as como en el esquema m as general de discriminaci on con margen de error. A continuaci on, este tipo de automatizaci on en tareas de discriminaci on se lleva un paso m as all a. Entendiendo una m aquina programable como un dispositivo entrenado con informaci on cu antica que es capaz de realizar una tarea espec ca, propongo una m aquina de aprendizaje cu antico para clasi car estados de qubit que no requiere una memoria cu antica para almacenar los qubits de los programas, permitiendo as repetidos usos de la m aquina sin necesidad de volver a entrenarla. Demuestro que dicha m aquina de aprendizaje es capaz de clasi car el estado de un qubit con la m nima tasa de errores admitida por la mec anica cu antica, y por tanto puede ser reusada manteniendo un rendimiento optimo. Tambi en estudio un esquema de aprendizaje similar para estados de luz coherente. Este se presenta en un contexto de lectura de una memoria cl asica mediante se~nales coherentes correlacionadas cl asicamente cuando estas son producidas por una fuente imperfecta y, por lo tanto, en un estado con un cierto grado de incertidumbre asociado. Muestro que la extracci on de la informaci on almacenada en la memoria es m as e ciente si la incertidumbre se trata de una forma completamente cu antica. Por ultimo, analizo la estructura matem atica de las medidas cu anticas generalizadas, omnipresentes en todos los temas tratados en esta tesis. Propongo un algoritmo constructivo y e ciente para descomponer cualquier medida cu antica en una combinaci on convexa estad sticamente equivalente de medidas m as simples (extremales). Estas en principio son menos costosas de implementar en un laboratorio y, por tanto, pueden ser utiles en situaciones pr acticas donde a menudo prevalece una perspectiva de recursos m nimos.
Discriminating the state of a quantum system among a number of options is one of the most fundamental operations in quantum information theory. A primal feature of quantum theory is that, when two possible quantum states are nonorthogonal, no conceivable measurement of the system can determine its state with certainty. Quantum indeterminism so demands a probabilistic approach to the task of discriminating between quantum states. The usual setting considers that the possible states of the system are known. In this thesis, I analyze the role of the prior information available in facing a quantum state discrimination problem, and consider scenarios where the information regarding the possible states is incomplete. In front of a complete ignorance of the possible states' identity, I discuss a quantum programmable discrimination machine for qubit states that accepts this information as input programs using a quantum encoding, rather than just as a classical description. This \classical" ignorance is taken into account in the design, and, as a consequence, the machine is not case-speci c but it is able to handle discrimination tasks between any pair of possible qubits, once conveniently programmed through quantum inputs. The optimal per- formance of programmable machines is studied in detail for general qubit states when several copies of the states are provided, in the main schemes of unambiguous and minimum-error discrimination as well as in the more general scheme of discrimination with an error margin. Then, this type of automation in discrimination tasks is taken further. By realizing a programmable machine as a device that is trained through quantum information to perform a speci c task, I propose a quantum learning machine for classifying qubit states that does not require a quantum memory to store the qubit programs. I prove that such learning machine classi es the state of a qubit with the minimum-error rate that quantum mechanics permits, thus allowing for several optimal uses of the machine without the need of retraining. A similar learning scheme is also discussed for coherent states of light. I present it in the context of the readout of a classical memory by means of classically correlated coherent signals, when these are produced by an imperfect source and hence their state has some uncertainty associated. I show that the retrieval of information stored in the memory can be carried out more accurately when fully general quantum measurements are used. Finally, I analyse the mathematical structure of generalized quantum measurements, ubiquitous in all the topics covered in this thesis. I pro- pose a constructive and e cient algorithm to decompose any given quantum measurement into a statistically equivalent convex combination of simpler (extremal) measurements, which are in principle less costly to implement in a laboratory. Being able to compute this type of measurement decomposi- tions becomes useful in practical situations, where often a minimum-resources perspective prevails.
APA, Harvard, Vancouver, ISO, and other styles
10

Quirk, Rachel Helen. "Fronto-striatal substrates of discrimination learning in the rat." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621238.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Discrimination learning"

1

Altonji, Joseph G. Employer learning and statistical discrimination. Cambridge, MA: National Bureau of Economic Research, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Jaeger, Thomas V. Opiate receptor blockade and discrimination learning. Ottawa: National Library of Canada, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Great Britain. Cabinet Office. Equal Opportunities Division. Equal opportunities learning programme. London: H.M.S.O., 1992.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ochoa, Gilda L. Learning from Latino teachers. San Francisco, CA: Jossey-Bass, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Carneiro, Pedro. Labor market discrimination and racial differences in premarket factors. Bonn, Germany: IZA, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Evamy, Barbara. Auditory & visual discrimination exercises: A teacher's aid. [Great Britain]: B. Evamy, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ann, Bagnall, and Northern Ireland Post Qualifying Education and Training Partnership., eds. Difference, diversity and discrimination: An independent learning pack for positive practice. Belfast: Northern Ireland Post Qualifying Education and Training Partnership, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Glennon, Richard A. Drug discrimination: Applications to medicinal chemistry and drug studies. Hoboken, New Jersey: Wiley, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Levy, Gary D. Gender schemas and discrimination learning: a new twist on an old paradigm. Syracuse: Syracuse University, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

1971-, Geeta K., Asia Pacific Advisory Forum on Judicial Education on Equality Issues., and Sakshi (Organization), eds. Walking wisdom: A creative learning experience. Gurgaon: Sakshi, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Discrimination learning"

1

Zonneveld, Kimberley, and Ivy Chong. "Discrimination Learning." In Encyclopedia of Child Behavior and Development, 508–9. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_864.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Jebara, Tony. "Latent Discrimination." In Machine Learning, 131–69. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Jebara, Tony. "Maximum Entropy Discrimination." In Machine Learning, 61–98. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Rose, Jonas, and Robert Schmidt. "Discrimination Learning Model." In Encyclopedia of the Sciences of Learning, 1013–15. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_343.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Järbe, Torbjörn U. C. "Drug Discrimination Learning." In Experimental Psychopharmacology, 433–79. Totowa, NJ: Humana Press, 1987. http://dx.doi.org/10.1007/978-1-59259-461-0_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Levine, Marvin. "Human Discrimination Learning." In A Cognitive Theory of Learning, 213–20. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Frankel, Fred, Marvin Levine, and David Karpf. "Human Discrimination Learning." In A Cognitive Theory of Learning, 203–12. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

de Campos, Gabriela Ribeiro, and Cláudia Helena Daher. "Cultural Diversity in Teaching and Learning Foreign Languages: Opening up to Dialogue and Understanding Plural Identities." In From Discriminating to Discrimination, 83–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13544-6_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Miikkulainen, Risto, and Rada Mihalcea. "Word Sense Discrimination." In Encyclopedia of Machine Learning, 1030. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_883.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Young, Michael. "Generalization Versus Discrimination." In Encyclopedia of the Sciences of Learning, 1349–52. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1030.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Discrimination learning"

1

Frej, Jibril, Philippe Mulhem, Didier Schwab, and Jean-Pierre Chevallet. "Learning Term Discrimination." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401211.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Dong, Nanqing, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, and Steven McDonagh. "Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/406.

Full text
Abstract:
This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs. While supervised image reconstruction aims to minimize residual terms directly, RCL alternatively builds a connection between residuals and CL by defining a novel instance discrimination pretext task, using residuals as the discriminative feature. Our formulation mitigates the severe task misalignment between instance discrimination pretext tasks and downstream image reconstruction tasks, present in existing CL frameworks. Experimentally, we find that RCL can learn robust and transferable representations that improve the performance of various downstream tasks, such as denoising and super resolution, in comparison with recent self-supervised methods designed specifically for noisy inputs. Additionally, our unsupervised pre-training can significantly reduce annotation costs whilst maintaining performance competitive with fully-supervised image reconstruction.
APA, Harvard, Vancouver, ISO, and other styles
3

Kamiran, Faisal, Toon Calders, and Mykola Pechenizkiy. "Discrimination Aware Decision Tree Learning." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.50.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Guerreiro, Rui F. C., and Pedro M. Q. Aguiar. "Learning simple texture discrimination filters." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5652648.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Grenon, Izabelle, Chris Sheppard, and John Archibald. "Discrimination training for learning sound contrasts." In ISAPh 2018 International Symposium on Applied Phonetics. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/isaph.2018-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Nakao, Hitoshi, Taku Akase, and Lifeng Zhang. "Product Discrimination System Using Deep Learning." In The 7th International Conference on Intelligent Systems and Image Processing 2019. The Institute of Industrial Application Engineers, 2019. http://dx.doi.org/10.12792/icisip2019.042.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wang, Wei, and Min-Ling Zhang. "Partial Label Learning with Discrimination Augmentation." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539363.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wu, Pangjing, and Xiaodong Li. "Market Style Discrimination via Ensemble Learning." In 2022 IEEE 13th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2022. http://dx.doi.org/10.1109/icsess54813.2022.9930158.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu. "Active Discriminative Network Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/296.

Full text
Abstract:
Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.
APA, Harvard, Vancouver, ISO, and other styles
10

Dan, Yangjie, Fan Xu, and Mingwen Wang. "End-to-End Chinese Dialect Discrimination with Self-Attention." In 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111425.

Full text
Abstract:
Dialect discrimination has an important practical significance for protecting inheritance of dialects. The traditional dialect discrimination methods pay much attention to the underlying acoustic features, and ignore the meaning of the pronunciation itself, resulting in low performance. This paper systematically explores the validity of the pronunciation features of dialect speech composed of phoneme sequence information for dialect discrimination, and designs an end-to-end dialect discrimination model based on the multi-head self-attention mechanism. Specifically, we first adopt the residual convolution neural network and the multihead self-attention mechanism to effectively extract the phoneme sequence features unique to different dialects to compose the novel phonetic features. Then, we perform dialect discrimination based on the extracted phonetic features using the self-attention mechanism and bi-directional long short-term memory networks. The experimental results on the large-scale benchmark 10-way Chinese dialect corpus released by IFLYTEK 1 show that our model outperforms the state-of-the-art alternatives by large margin.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Discrimination learning"

1

Altonji, Joseph, and Charles Pierret. Employer Learning and Statistical Discrimination. Cambridge, MA: National Bureau of Economic Research, November 1997. http://dx.doi.org/10.3386/w6279.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pathak, Aditya Ranjan, and Anandita Pathak. Learning Culture, Unlearning Stereotypes: Ending Discrimination Torwards India’s Northeast. Critical Asian Studies, June 2021. http://dx.doi.org/10.52698/ggpu8908.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wurtz, R., and A. Kaplan. Statistical and Machine-Learning Classifier Framework to Improve Pulse Shape Discrimination System Design. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1236750.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Milanfar, Peyman. Detection and Discrimination at the Intersection of Statistical Signal Processing and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada481960.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Freed, Danielle. K4D’s Tax and Gender Learning Journey Boosting Social Reform in Pakistan. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.163.

Full text
Abstract:
As a means to reduce discrimination and promote the economic empowerment of women, there is a growing understanding that tax policy, tax administration and tax research need to be gender transformative. Recognising this need, the Foreign, Commonwealth and Development Office (FCDO) is reshaping and building its approach to tax and gender programming. K4D’s Tax and Gender Learning Journey brought together tax and gender teams to identify other tax and gender stakeholders and collaboratively craft a future approach to tax and gender for FCDO and partners. Initial exploration of the early impact from activities that have taken place amongst partner organisations in Pakistan suggests K4D’s inputs have the potential to bolster intended social reforms across the country’s revenue and other government departments.
APA, Harvard, Vancouver, ISO, and other styles
6

Freed, Danielle. K4D Strengthens Partners’ Ability to Deliver Improved Results for Inclusion in Crises. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.161.

Full text
Abstract:
Achieving Sustainable Development Goals requires the inclusion of the most vulnerable people affected by intersecting inequalities. Inclusion is an approach and objective that serves to counter structural discrimination and enable affected people and communities to withstand the causes and impacts of crises. Recognising the need to address this issue, the UK government has sought to implement a range of commitments relating to inclusion in its programme and policy responses to crises. The K4D Inclusion in Crises Learning Journey has played a key role in supporting reflection on the opportunities and challenges for operationalising these commitments and equipping participants with the tools needed to make sure programmes can deliver high impact results, improving the lives and wellbeing of people who are marginalised and crisis-affected.
APA, Harvard, Vancouver, ISO, and other styles
7

Huynh, Tuyen N. Discriminative Learning with Markov Logic Networks. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada512664.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Carter, Becky. Analysing Intersecting Social Inequalities in Crisis Settings. Institute of Development Studies (IDS), January 2022. http://dx.doi.org/10.19088/k4d.2022.003.

Full text
Abstract:
Analysis of intersecting social inequalities is key to effective, inclusive interventions in crisis settings. Gender equality and social inclusion analytical frameworks provide key research questions and participatory methodologies which seek to understand: Who is excluded? How are they excluded? Why are they excluded? What can be done to address this and support greater inclusion? There is a focus on underlying power dynamics, drivers of marginalisation, and entry points for external support. This rapid review presents a summary of relevant analytical frameworks and good practice for analysing intersecting social inequalities in crisis settings. The focus is on how to undertake contextual analysis of the vulnerabilities and needs of people in crises that are shaped by overlapping and compounding social inequalities, arising from discrimination based on gender, age, disability, sexual orientation, gender identity and/or expression and sex characteristics, ethnicity and religion (among other identifiers). The review draws on and presents prior research that identified relevant analytical frameworks, learning and key resources on how to undertake this type of analysis, through a rapid literature search and input by key experts. It summarises a range of frameworks relevant for analysing intersecting social inequalities in crisis settings, developed for various development, humanitarian and peacebuilding objectives. It was harder to find published learning from undertaking this analysis that focuses specifically on crisis settings, but it was possible to draw findings from some individual case studies as well as relevant summaries of learning presented in the analytical frameworks and other guidance materials.
APA, Harvard, Vancouver, ISO, and other styles
9

Arias, Omar, Gustavo Yamada, and Luis Tejerina. Education, Family Background and Racial Earnings Inequality in Brazil. Inter-American Development Bank, September 2002. http://dx.doi.org/10.18235/0012219.

Full text
Abstract:
This study combines survey data with annual state data on pupil-teacher ratios covering broadly the period 1940-90 to investigate the role of race, family background and education (both the quantity and quality) in explaining earnings inequality between whites and the African descendent population (pretos and pardos) in Brazil. The authors estimate quantile Mincer earnings equations to go beyond the usual racial average earnings gaps decompositions. The main findings indicate that differences in human capital, including parental education and education quality, and in its returns, account for most but not all of the earnings gap between the African descendent population and whites. There is evidence of potential greater pay discrimination at the higher salary jobs at any given skill level. The authors also find that returns to education vary significantly across workers. The results suggest that while equalizing access to quality education, including improved early learning environments, is key to reduce inter-racial earnings inequality in Brazil, specific policies are also needed to facilitate non-whites equal access to good quality jobs.
APA, Harvard, Vancouver, ISO, and other styles
10

Nguyen, Minh H., Lorenzo Torresani, Fernando de la Torre, and Carsten Rother. Weakly Supervised Discriminative Localization and Classification: A Joint Learning Process. Fort Belvoir, VA: Defense Technical Information Center, July 2009. http://dx.doi.org/10.21236/ada507101.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Discrimination learning' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography