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Relevant bibliographies by topics / Self-paced

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Academic literature on the topic 'Self-paced'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Self-paced"

1

Yu, Tingzhao, Chaoxu Guo, Lingfeng Wang, Shiming Xiang, and Chunhong Pan. "Self-Paced AutoEncoder." IEEE Signal Processing Letters 25, no. 7 (July 2018): 1054–58. http://dx.doi.org/10.1109/lsp.2018.2843295.

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Stowers, Michael P., and Martin Tessmer. "Self-paced instruction." New Directions for Higher Education 1986, no. 56 (1986): 17–26. http://dx.doi.org/10.1002/he.36919865604.

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Wessel, Karl, Thomas Zeffiro, Camilo Toro, and Mark Hallett. "Self-Paced Versus Metronome-Paced 7 Finger Movements." Journal of Neuroimaging 7, no. 3 (July 1997): 145–51. http://dx.doi.org/10.1111/jon199773145.

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Edwards, Clayton M. "Self-Paced Mathematical Instruction." Mathematics Teaching in the Middle School 19, no. 4 (November 2013): 230–36. http://dx.doi.org/10.5951/mathteacmiddscho.19.4.0230.

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MENG, Deyu, Zongben XU, and Jun SHU. "Meta self-paced learning." SCIENTIA SINICA Informationis 50, no. 6 (June 1, 2020): 781–93. http://dx.doi.org/10.1360/ssi-2020-0005.

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Holroyd, Tom, Matt Nielsen, S. Miyauchi, and T. Yanagida. "2P219MEG during self-paced and externally paced rhythmic tapping." Seibutsu Butsuri 41, supplement (2001): S150. http://dx.doi.org/10.2142/biophys.41.s150_3.

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Schlegel, Betina, and Margarita Beneke. "A Study of Self-Paced and Machine-Paced Inspection." Proceedings of the Human Factors Society Annual Meeting 30, no. 5 (September 1986): 471–75. http://dx.doi.org/10.1177/154193128603000514.

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A study was conducted to investigate the performance of inspectors under different conditions using computer generated visual test items. The purpose of the study was to compare performance for self-paced vs. machine-paced inspection tasks. The factors considered were searching for a single type of flaw vs. three types of flaws, the viewing time for machine-paced inspection, and the type of instruction for self-paced inspection. The results showed that performance was better when subjects searched for only one type of flaw. Performance improved with increased viewing time, but was not affected by the type of instruction. There was no difference between self-paced and machine-paced inspection provided the viewing time was sufficient. Overall, performance in self-paced inspection was better.

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Ren, Yazhou, Xiaofan Que, Dezhong Yao, and Zenglin Xu. "Self-paced multi-task clustering." Neurocomputing 350 (July 2019): 212–20. http://dx.doi.org/10.1016/j.neucom.2019.03.062.

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Klenk, Virginia. "Self-Paced Logic Without Computers." Teaching Philosophy 9, no. 3 (1986): 239–46. http://dx.doi.org/10.5840/teachphil19869338.

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Rhode, Jason, and Murali Krishnamurthi. "Preparing Faculty to Teach Online: Recommendations for Developing Self-Paced Training." International Journal of Information and Education Technology 6, no. 5 (2016): 376–82. http://dx.doi.org/10.7763/ijiet.2016.v6.717.

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Dissertations / Theses on the topic "Self-paced"

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Braet, David Christopher. "Principles for designing online self-paced corporate training." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1473185.

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Bashashati, Hossein. "A user-customized self-paced brain computer interface." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61248.

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Much attention has been directed towards synchronous Brain Computer Interfaces (BCIs). For these BCIs, the user can only operate the system during specific system-defined periods. Self-paced BCIs, however, allow users to operate the system at any time he/she wishes. The classification of Electroencephalography (EEG) signals in self-paced BCIs is extremely challenging, as the BCI system does not have any clue about the start time of a control task. Also, the data contains a large number of periods during which the user has no intention to control the BCI. For sensory motor self-paced BCIs (focus of this thesis), the brain of a user goes through several well-defined internal state changes while performing a mental task. Designing classifiers that exploit such temporal correlations in EEG data can enhance the performance of BCIs. It is also important to customize these BCIs for each user, because the brain characteristics of different people are not the same. In this thesis, we first develop a unified comparison framework to compare the performance of different classifiers in sensory motor BCIs followed by rigorous statistical tests. This study is the largest of its kind as it has been performed on 29 subjects of synchronous and self-paced BCIs. We then develop a Bayesian optimization-based strategy that automatically customizes a synchronous BCI based on the brain characteristics of each individual subject. Our results show that our automated algorithm (which relies on less sophisticated feature extraction and classification methods) yields similar or superior results compared to the best performing designs in the literature. We then propose an algorithm that can capture the time dynamics of the EEG signal for self-paced BCI systems. We show that this algorithm yields better results compared to several well-known algorithms, over 13 self-paced BCI subjects. Finally, we propose a fully automatic, scalable algorithm that customizes a self-paced BCI system based on the brain characteristics of each user and at the same time captures the dynamics of the EEG signal. Our final algorithm is an important step towards transitioning BCIs from research environments to real-life applications, where automatic, scalable and easy to use systems are needed.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

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Jackson, Robin C. "A behavioural analysis of 'choking' in self-paced skills." Thesis, University of St Andrews, 1998. http://hdl.handle.net/10023/3714.

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This thesis is about 'choking' in self-paced skills. Choking refers to 'the occurrence of inferior performance despite striving and incentives for superior performance' (Baumeister and Steinhilber, 1986, p. 361). Self-paced skills are skills in which performance is initiated by the athlete. This research set out to investigate the cause of choking in self-paced skills within the theoretical framework of behaviour analysis. The main focus of the research relates to the distinction between behaviour under the control of verbal antecedents (rule-governed behaviour) and behaviour that is shaped by its consequences (contingency-shaped behaviour). It was originally hypothesised that the insensitivity of rule-governed behaviour to changes in the contingencies of reinforcement could he beneficial in situations where these changes led to greater performance pressure. Specifically, it was predicted that performance under the control of verbal antecedents would be less susceptible to choking. In the first experiment, no support was found for the hypothesis and, furthermore, rule-governed performance appeared to be inferior to contingency-shaped performance in the early stages of acquisition. In light of these results, and after a detailed examination of the behaviour analysis distinction between these two forms of behaviour, evidence was presented which suggested that verbal control of the topography, or form, of behaviour would be likely to disrupt performance in self-paced skills. In subsequent experiments, it was found that using simple verbal cues was an effective means of preventing choking under pressure. It was hypothesised that the function of these cues was in preventing reinvestment of too many technical instructions in the moments before performance initiation. The assumptions upon which the reinvestment theory of choking is based were also examined with results providing general support for the theory but also suggesting that it needs to be refined to account for verbal antecedents that do not disrupt performance.

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Thomas, Kevin. "Regulation of power output during self-paced cycling exercise." Thesis, Northumbria University, 2013. http://nrl.northumbria.ac.uk/15122/.

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Fatigue is a universal phenomenon with functional and perceptual consequences. The study of fatigue in the exercise sciences has historically focussed on factors that limit performance during exercise. More recent proposals have shifted the focus of this understanding to examine how intensity is regulated during exercise through the study of the pacing strategy, which has both physiological and practical consequences. The aim of this thesis was to investigate the biological basis of self-pacing and the optimum pacing strategy for endurance time-trial events. Study 1 assessed the reproducibility of the pacing strategy and the consistency of the performance, perceptual and physiological response during self-paced time-trial exercise in well-trained cyclists. This study demonstrated the existence of a global pacing strategy that was reproducible on repeat 20 km cycling time-trials (TTs), and consistent between 4, 20 and 40 km TTs. The performance, perceptual and physiological response was also reproducible, confirming the feasibility of studying manipulations of the self-pacing strategy and the subsequent impact on these variables. Studies 2 and 3 adopted a model whereby participant’s best self-paced TT performance was used to set time- and work-matched exercise bouts to study the effect of even- and variable-pacing. These studies revealed that a variable-pacing strategy that contains frequent periods of high-intensity exercise resulted in an augmented physiological response and higher perception of exertion compared to time- and work-matched even- and self-paced exercise. Conversely, even-pacing resulted in attenuation in the metabolic and perceptual cost of the bout, but only when the self-selected pacing strategy was sub-optimal. When self-pacing was optimal, time- and work-matched even-pacing resulted in cumulative metabolic stress that caused early exercise termination. In study 4 the biological basis to fatigue during 4, 20 and 40 km TTs was assessed. This study demonstrated that the contribution of central and peripheral mechanisms of fatigue during self-paced exercise is task-dependent. Specifically, the shorter, higher intensity 4 km time-trials were characterised by a greater degree of peripheral fatigue and less central fatigue compared to longer, lower intensity 20 and 40 km time-trials where less peripheral and more central fatigue was observed. The supraspinal contribution to fatigue was alsomgreater during longer TT exercise. These studies have provided novel insight in to the biological factors that underpin the regulation of self-paced exercise, and the optimum pacing strategy for endurance TT events.

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Tsui, Chun Sing Louis. "Adaptive Self-paced Brain-Actuated Control of Mobility Devices." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504838.

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Flaherty, Michelle A. "The effects of Sopite Syndrome on self-paced airsickness desensitization program." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA354296.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 1998.
"September 1998." Thesis advisor(s): John K. Schmidt, Robert R. Read. Includes bibliographical references (p. 57-59). Also available online.

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Katomeri, Magdalena. "Acute effects of self-paced walking on smoking withdrawal and cravings." Thesis, University of Plymouth, 2007. http://hdl.handle.net/10026.1/2537.

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High relapse rates among smokers attempting to quit have been linked with failure to cope with cigarette cravings and withdrawal symptoms. Smoking cravings and withdrawal behavior is known to be influenced by stress and exposure to smoking cues. Exercise appears to reduce cigarette cravings and withdrawal symptoms. However no study has investigated the effects of exercise on cue-elicited cravings and withdrawal symptoms, or on ad libitum smoking behavior. Aim: Four experimental studies were designed to examine the effects of exercise on smoking behavior. Initial studies focused on the effects of a single session of brisk walk on cigarette cravings and withdrawal symptom. Further research examined the effects of the walk on withdrawal symptoms and cravings and response to smoking cues and time spent on ad libitum smoking. Methods: All studies examined the effects of a short bout of brisk walking (15- 20 minutes) versus a passive control condition. For the purposes of the experiments, participants were temporarily smoking abstinent or non-abstinent. Multiple and single item measures of cravings and withdrawal, as well as different experimental designs were used in order to enhance the reliability of the findings. In Studies 2,3, and 4 after the exercise (or the control) participants were exposed to smoking cues. In these studies after the laboratory session, ad libitum smoking was determined from the subject's cell phone text message. Results: Cigarette cravings, withdrawal symptoms and negative affect decreased rapidly during exercise and remained reduced for 20-50 minutes after exercise. This pattern was robust across the four studies, across different abstinent periods, across single and multiple measures of cravings and withdrawal symptoms, across experimental designs and across samples. Furthermore, it was shown that exercise attenuated increases in cue elicited cravings and withdrawal symptoms. Results also indicated a two- to threefold longer time to the next cigarette following exercise. Exercise produced these effects by mimicking the relaxing effects of smoking. Conclusions: Short bouts of brisk walk are recommended as an aid to managing cigarette cravings and withdrawal symptoms.

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Faradji, Farhad. "Design of a self-paced brain-computer interface based on mental tasks." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42330.

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People with very severe motor-disabilities have to accept a much-reduced quality of life. Unfortunately, these people cannot use assistive devices as present devices require motor activities. Brain-computer interfaces (BCIs) provide an alternative means of communication between the brain and assistive devices. There are two types of BCIs, synchronous and self-paced. Self-paced BCIs are more practical as they can be used at any time. The vast majority of existing self-paced BCIs are activated by real, attempted, or imagined movements. Few are activated by mental tasks. The high false positive rates (FPRs) of existing self-paced BCIs render them impractical. There are no self-paced BCIs based on motor movements that have low FPRs. However, self-paced BCIs with low FPRs based on mental tasks have been proposed. Designing a self-paced mental task-based BCI with a zero or near zero FPR and a reasonable true positive rate (TPR) is the goal of this thesis. We investigated the feasibility of having a self-paced mental task-based BCI with a zero or near zero FPR. The EEG signals from 6 electrodes of 4 subjects performing 4 mental tasks are used. Features were extracted using autoregressive modeling. Different classifiers were tested. The results were promising in that zero FPRs were obtained. The data used, however, had not been collected in a self-paced paradigm. We then collected the EEG signals from 29 channels of 4 subjects performing 4 mental tasks in a self-paced paradigm. We evaluated the performance of our BCI using this dataset. It yielded an average TPR of 67.26% and zero FPR. To make the system practical, we decrease the number of channels from 29 to 7 and 5, using 2 approaches that yield local optimal results. The average TPR is sufficiently high (54.60% and 59.98% for systems with 5 and 7 channels) while the FPRs remain zero. We also study the effects on the performance, as the segment length is varied. For the 7-channel BCIs, the optimum length is between 1 and 2.5 sec. The average TPR is improved to 63.47%. The FPRs are zero. We also show that our BCIs are robust to artifacts.

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Satti, Abdul Rehman. "Fast adaptive signal processing for intelligent multistate self-paced brain-computer interfaces." Thesis, University of Ulster, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530506.

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Peterson, Rex H. "Self-paced instruction in the U.S. Army : a boon or a dilemma." Virtual Press, 1992. http://liblink.bsu.edu/uhtbin/catkey/833007.

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The study reviews how self-paced training programs were developed by the U.S. Army for Initial Entry Training (IET), and conducted at the various service schools. In early 1975, after a long validation effort, the Training and Doctrine Command (TRADOC) published guidance to the service schools and training centers to begin self-paced training. Most service schools sought to follow this guidance in one form or another which demanded a job analysis, systematic development, and tryouts on typical soldiers. Through the years 1975-78, the service schools worked diligently to develop, evaluate and implement self-paced instruction in most of the job specialties of the U.S Army. Around 1978, complaints from field units began to find their way to the Department of the Army and TRADOC. The units complained that they were receiving too many unqualified soldiers from the advanced individual training courses. In response to these complaints, an in depth study was conducted at many of the schools and training centers. As a result of this study, in June 1983, the TRADOC Commander of TRADOC directed the service schools to move from their self-pace efforts toward group-pace.Contributing factors that caused the U.S Army to move away from self-paced instruction were: inherent lack of reenforcement training for the students in skills they had learned in basic training (soldierization skills); students entering the U.S. Army had poor reading skills, lack of motivation, and little formal education; there were problems in scheduling students through the school's "pipeline" and on to their first unit of assignment; and a shortage of properly trained developers and instructors.The U.S. Army's decision to introduce self-paced instruction, although not successful, was not wasted time and resources. It laid out a course that has led to group-paced instruction. The victory, called "Desert Storm", has proven the United States has been very successful with its training stratagem and has a well trained military force.This study was prepared as a historiography, and as such, recorded events as they occurred along a time continuum, and analyzed, correlated, and drew conclusions from those events.3
Department of Educational Leadership

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Books on the topic "Self-paced"

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Self-paced business mathematics. 3rd ed. Boston, Mass: Kent Pub. Co., 1986.

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Matz, Leonard M. Self-paced bank investment training. Austin, Tex: Sheshunoff Information Services, 1998.

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1973-, Thomas Orin, ed. MCSA/MCSE self-paced training kit. Redmond, WA: Microsoft Press, 2004.

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Mastering internetworking: Self-paced learning series. Fremont, CA: Numidia Press, 1992.

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Everhardt, Nelson F. Self-paced anti-money laundering training. Austin, Tex: Sheshunoff, 2004.

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Ethan, Wilansky, and Microsoft Corporation, eds. MCSA/MCSE self-paced training kit. 2nd ed. Redmond, WA: Microsoft Press, 2003.

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Press, Nancy Ottman. Interlibrary loans: A self-paced manual. Seattle: Pacific Northwest Regional Health Sciences Library Service, 1985.

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Sheldon, Bob. MCSA/MCSE self-paced training kit. 2nd ed. Redmond, WA: Microsoft Press, 2003.

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Self-paced phonics: A text for educators. 3rd ed. Upper Saddle River, N.J: Merrill/Prentice Hall, 2003.

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Montanus, Mark. Computer literacy: Beginning : a self-paced course. Dubuque, Iowa: Kendall/Hunt, 1986.

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Book chapters on the topic "Self-paced"

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Zięba, Maciej, Jakub M. Tomczak, and Jerzy Świątek. "Self-paced Learning for Imbalanced Data." In Intelligent Information and Database Systems, 564–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49381-6_54.

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Kitajima, Munéo. "A CCE Study: Slow Self-Paced Navigation." In Memory and Action Selection in Human-Machine Interaction, 75–91. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119268628.ch6.

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Mital, Anil, Ashok Chalaka, and Waldemar Karwowski. "The Demands and Responses of Machine-Paced and Self-Paced Material Handling Tasks." In Toward the Factory of the Future, 524–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82580-4_88.

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Zhang, Xiao-Yu, Shupeng Wang, Yanfei Lv, Peng Li, and Haiping Wang. "Augmented Self-paced Learning with Generative Adversarial Networks." In Lecture Notes in Computer Science, 450–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93713-7_39.

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Lyu, Gengyu, Songhe Feng, Yi Jin, and Yidong Li. "Partial Label Learning via Self-Paced Curriculum Strategy." In Machine Learning and Knowledge Discovery in Databases, 489–505. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_29.

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Wang, Wenzhe, Ruiwei Feng, Xuechen Liu, Yifei Lu, Yanjie Wang, Ruoqian Guo, Zhiwen Lin, Tingting Chen, Danny Z. Chen, and Jian Wu. "Deep Active Self-paced Learning for Biomedical Image Analysis." In Intelligent Systems Reference Library, 95–110. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32606-7_6.

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Zhang, Xianchao, Heng Shi, Yuangang Li, and Wenxin Liang. "SPGLAD: A Self-paced Learning-Based Crowdsourcing Classification Model." In Lecture Notes in Computer Science, 189–201. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67274-8_17.

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Ren, Yazhou, Xin Yan, Zechuan Hu, and Zenglin Xu. "Self-Paced Multi-Task Multi-View Capped-norm Clustering." In Neural Information Processing, 205–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04212-7_18.

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Niu, Yong, and Xiaolan Fu. "The Effect of Mood on Self-paced Study Time." In Affective Computing and Intelligent Interaction, 646–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573548_83.

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Punsawad, Yunyong, and Yodchanan Wongsawat. "Self-paced Emotional Imagery-Based Brain Computer Interface System." In 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6), 567–71. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4361-1_97.

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Conference papers on the topic "Self-paced"

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Liu, Youfa, Bo Du, and Lefei Zhang. "Self-Paced Subspace Clustering." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00068.

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Gao, Hongchang, and Heng Huang. "Self-Paced Network Embedding." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219819.3220041.

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Han, Longfei, Dingwen Zhang, Dong Huang, Xiaojun Chang, Jun Ren, Senlin Luo, and Junwei Han. "Self-paced Mixture of Regressions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/252.

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Mixture of regressions (MoR) is the well-established and effective approach to model discontinuous and heterogeneous data in regression problems. Existing MoR approaches assume smooth joint distribution for its good anlaytic properties. However, such assumption makes existing MoR very sensitive to intra-component outliers (the noisy training data residing in certain components) and the inter-component imbalance (the different amounts of training data in different components). In this paper, we make the earliest effort on Self-paced Learning (SPL) in MoR, i.e., Self-paced mixture of regressions (SPMoR) model. We propose a novel self-paced regularizer based on the Exclusive LASSO, which improves inter-component balance of training data. As a robust learning regime, SPL pursues confidence sample reasoning. To demonstrate the effectiveness of SPMoR, we conducted experiments on both the sythetic examples and real-world applications to age estimation and glucose estimation. The results show that SPMoR outperforms the state-of-the-arts methods.

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Li, Hao, and Maoguo Gong. "Self-paced Convolutional Neural Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/293.

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Convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks. In order to distinguish the reliable data from the noisy and confusing data, we improve CNNs with self-paced learning (SPL) for enhancing the learning robustness of CNNs. In the proposed self-paced convolutional network (SPCN), each sample is assigned to a weight to reflect the easiness of the sample. Then a dynamic self-paced function is incorporated into the leaning objective of CNN to jointly learn the parameters of CNN and the latent weight variable. SPCN learns the samples from easy to complex and the sample weights can dynamically control the learning rates for converging to better values. To gain more insights of SPCN, theoretical studies are conducted to show that SPCN converges to a stationary solution and is robust to the noisy and confusing data. Experimental results on MNIST and rectangles datasets demonstrate that the proposed method outperforms baseline methods.

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Campbell, Jennifer, Andrew Petersen, and Jacqueline Smith. "Self-paced Mastery Learning CS1." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287324.3287481.

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Peterson, Peter, and Greg Fellin. "Self-paced training 2000 (poster session)." In the 27th annual ACM SIGUCCS conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/337043.337174.

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Liang, Jian, Zhihang Li, Dong Cao, Ran He, and Jingdong Wang. "Self-Paced Cross-Modal Subspace Matching." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2911451.2911527.

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Peterson, Peter L. "Self-paced training expanding educational opportunities." In the 24th annual ACM SIGUCCS conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/262051.262096.

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Xin, Xiaomeng, Xindi Wu, Yuechen Wang, and Jinjun Wang. "Deep Self-Paced Learning for Semi-Supervised Person Re-Identification Using Multi-View Self-Paced Clustering." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803290.

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Tang, Ye, Yu-Bin Yang, and Yang Gao. "Self-paced dictionary learning for image classification." In the 20th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393347.2396324.

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