Dissertations / Theses: 'Support du superviseur'

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Bhar, Jane (Jane Frances) Carleton University Dissertation Management Studies. "Developing a measure of supervisor support." Ottawa, 1995.

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Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.

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An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition of the above process improves the separability until there is no misclassification. Variations on this type of clustering approach are shown.

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Pehrson, Jakob, and Sara Lindstrand. "Support Unit Classification through Supervised Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281537.

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The purpose of this article is to evaluate the impact a supervised machine learning classification model can have on the process of internal customer support within a large digitized company. Chatbots are becoming a frequently used utility among digital services, though the true general impact is not always clear. The research is separated into the following two questions: (1) Which supervised machine learning algorithm of naïve Bayes, logistic regression, and neural networks can best predict the correct support a user needs and with what accuracy? And (2) What is the effect on the productivity and customer satisfaction of using machine learning to sort customer needs? The data was collected from the internal server database of a large digital company and was then trained on and tested with the three classification algorithms. Furthermore, a survey was collected with questions focused on understanding how the current system affects the involved employees. A first finding indicates that neural networks is the best suited model for the classification task. Though, when the scope and complexity was limited, naïve Bayes and logistic regression performed sufficiently. A second finding of the study is that the classification model potentially improves productivity given that the baseline is met. However, a difficulty exists in drawing conclusions on the exact effects on customer satisfaction since there are many aspects to take into account. Nevertheless, there is a good potential to achieve a positive net effect.
Syftet med artikeln är att utvärdera den påverkan som en klassificeringsmodell kan ha på den interna processen av kundtjänst inom ett stort digitaliserat företag. Chatbotar används allt mer frekvent bland digitala tjänster, även om den generella effekten inte alltid är tydlig. Studien är uppdelad i följande två frågeställningar: (1) Vilken klassificeringsalgoritm bland naive Bayes, logistisk regression, och neurala nätverk kan bäst förutspå den korrekta hjälpen en användare är i behov av och med vilken noggrannhet? Och (2) Vad är effekten på produktivitet och kundnöjdhet för användandet av maskininlärning för sortering av kundbehov? Data samlades från ett stort, digitalt företags interna databas och används sedan i träning och testning med de tre klassificeringsalgoritmerna. Vidare, en enkät skickades ut med fokus på att förstå hur det nuvarande systemet påverkar de berörda arbetarna. Ett första fynd indikerar att neurala nätverk är den mest lämpade modellen för klassificeringen. Däremot, när omfånget och komplexiteten var begränsat presenterade även naive Bayes och logistisk regression tillräckligt. Ett andra fynd av studien är att klassificeringen potentiellt förbättrar produktiviteten givet att baslinjen är mött. Däremot existerar en svårighet i att dra slutsatser om den exakta effekten på kundnöjdhet eftersom det finns många olika aspekter att ta hänsyn till. Likväl finns en god potential i att uppnå en positiv nettoeffekt.

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Shah, Anuj R. "Improving protein remote homology detection using supervised and semi-supervised support vector machines." Online access for everyone, 2008. http://www.dissertations.wsu.edu/Dissertations/Spring2008/A_Shah_042408.pdf.

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Youngcourt, Satoris Sabrina. "Examination of supervisor assessments of employee work-life conflict, supervisor support, and subsequent outcomes." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3180.

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Research in the work-life area has typically concerned individuals' assessments of their own conflict. The current study went beyond this by examining supervisor assessments of employee conflict and how they relate to the support given to employees. This support, traditionally measured using a unidimensional measure of support, was measured with a multidimensional measure that differentiates eight separate forms of support, including listening, emotional, emotional challenge, reality confirmation, task appreciation, task challenge, tangible assistance, and personal assistance support. Additionally, the amount of personal contact between the supervisor and the employee and the extent to which the supervisor likes the employee were examined as potential moderators of the relationship between supervisor assessments and the support given. Further, employee satisfaction with supervisor support, as well as the potential moderating role of the need for support on the relationship between the provided support and the employee's satisfaction with the support, were explored. Finally, employee satisfaction with the eight forms of support and subsequent outcomes (i.e., subsequent work-life conflict, job satisfaction, turnover intentions, organizational commitment, and job performance) as they relate to the provided support were examined. Data were collected from 114 pairs of employees and supervisors. Employees were assessed at two time periods two weeks apart whereas supervisors were assessed at one time period, within five days of the employee's first time period. Results showed that supervisor assessments of employee work-life conflict were either unrelated or negatively related to the eight forms of support. Additionally, it appears that when supervisors perceived employees as having a high degree of work-to-life conflict, they provided relatively high and relatively equal amounts of emotional challenge and reality confirmation support to employees regardless of how much they liked them. When supervisors perceived employee work-to-life conflict as being low, however, they provided significantly more emotional challenge and reality confirmation support when they liked the employee as opposed to when they did not like the employee. Furthermore, the relationship between emotional challenge support and job satisfaction was mediated by satisfaction with emotional challenge support, the relationship between task appreciation support and affective commitment was mediated by satisfaction with task appreciation support, and the relationship between task appreciation support and job satisfaction was mediated by satisfaction with task appreciation support. Finally, when emotional challenge support was provided, greater levels of support led to greater employee satisfaction, especially if there was a need for the support. However, when reality confirmation support was provided, employees were less satisfied with the support when a large amount of support was provided and the employees' need for support was low.

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Schenkel, Aubree A. "Perceptions of Supervisor Support for Work-Life Balance." Xavier University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=xavier1396713191.

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Yu, Chongxin Organisation &amp Management Australian School of Business UNSW. "The influences of HR effectiveness and supervisor support on workers." Awarded by:University of New South Wales. Organisation & Management, 2009. http://handle.unsw.edu.au/1959.4/44695.

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Migrant workers in China tend to suffer from inferior status and hardship in the workplace. Domestic private enterprises have become highly market-oriented and have been criticized for exploiting workers; however, some of them have started to pay increasing attention to motivating and retaining workers. The well-being of migrant workers is worthy of study. This study collected survey data from migrant workers in two Chinese private enterprises in the cosmetics industry, aiming to probe how a harmonious and supportive working environment may benefit workers. It is argued that HR???s assistance to line managers can be conveyed to workers via supervisors, leading to perceptions of a supportive working system (represented by the behaviour of HR, managers and supervisors). This kind of system is likely to promote employees??? identification with the organisation and social exchanges with organisational members. Further, these may improve workers??? psychological state and cooperative worker relations. This thesis starts by presenting migrant workers??? experience and discussing how HRM is practised in Chinese private enterprises. Studies of organisational support are introduced as a foundation to explore the influences of HR on employee outcomes???emotional exhaustion and co-worker assistance???through the mechanism of supervisor support. The results validate the substantial role of effective HR assistance to line managers and the role of the supportive supervisor in improving employees??? well-being and in facilitating helping behaviour among co-workers. Finally, implications for management practices and future research are considered.

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Anderson, Suzanne Michelle. "Influences of supervisor and peer support on transfer of training." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2802.

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Student employees (N=86) at a major research institution participated in a new hire orientation training and then responded to questionnaires measuring ten transfer behaviors and eight work environment constructs measuring support, frequency of contact, cohesion, and general means efficacy. Supervisor ratings of trainee performance were used to measure transfer behaviors.

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Sucharski, Ivan Laars. "Influencing employees' generalization of support and commitment from supervisor to organization." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 191 p, 2007. http://proquest.umi.com/pqdweb?did=1253510051&sid=2&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Benbrahim, Houda. "A fuzzy semi-supervised support vector machine approach to hypertext categorization." Thesis, University of Portsmouth, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494145.

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As the web expands exponentially, the need to put some order to its content becomes apparent. Hypertext categorization, that is the automatic classification of web documents into predefined classes, came to elevate humans from that task. The extra information available in a hypertext document poses new challenges for automatic categorization. HTML tags and linked neighbourhood all provide rich information for hypertext categorization that is no available in traditional text classification.

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Mansfield, Layla Rhiannon. "Organizational Calling and Safety: the Role of Workload and Supervisor Support." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4234.

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Research suggests that individuals who perceive their work as a calling (a deep passion and meaningfulness associated with a certain domain) experience a variety of positive outcomes such as occupational identification, career decidedness, and job satisfaction. Utilizing the tenets of Social Exchange Theory and the Job Demands Resources Model, I proposed that individuals with greater calling toward their occupation will report higher safety motivation and safety compliance. However, under conditions of high workload this relationship would be attenuated. Further, by the same rationale, individuals with lower calling will report lower safety outcomes, yet I proposed that this relationship is mitigated under conditions of high supervisor support. The study was conducted with a sample of 183 participants collected across three forests within the United States Forest Service. Although the hypotheses in the study were not supported, this study provides theoretical groundwork elucidating the link between calling and the examined outcome - safety. This, in turn, will aid in the development of a number of potential research avenues for safety scholars, with many practical implications. Further, an examination of calling with other collected variables within this industry provides avenues for future research in the calling domain. The investigation of moderators may help to explain the conflicting results found in the calling literature. Finally, this study furthers our understanding of safety, workload, and supervisor support within a "helping field."

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Eschleman, Kevin. "THE EFFECTS OF CAUSAL ATTRIBUTIONS ON SUBORDINATE RESPONSES TO SUPERVISOR SUPPORT." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1310416190.

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Halpern, Yonatan. "Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine." Thesis, New York University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10192124.

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Medical informatics plays an important role in precision medicine, delivering the right information to the right person, at the right time. With the introduction and widespread adoption of electronic medical records, in the United States and world-wide, there is now a tremendous amount of health data available for analysis.

Electronic record phenotyping refers to the task of determining, from an electronic medical record entry, a concise descriptor of the patient, comprising of their medical history, current problems, presentation, etc. In inferring such a phenotype descriptor from the record, a computer, in a sense, "understands'' the relevant parts of the record. These phenotypes can then be used in downstream applications such as cohort selection for retrospective studies, real-time clinical decision support, contextual displays, intelligent search, and precise alerting mechanisms.

We are faced with three main challenges:

First, the unstructured and incomplete nature of the data recorded in the electronic medical records requires special attention. Relevant information can be missing or written in an obscure way that the computer does not understand.

Second, the scale of the data makes it important to develop efficient methods at all steps of the machine learning pipeline, including data collection and labeling, model learning and inference.

Third, large parts of medicine are well understood by health professionals. How do we combine the expert knowledge of specialists with the statistical insights from the electronic medical record?

Probabilistic graphical models such as Bayesian networks provide a useful abstraction for quantifying uncertainty and describing complex dependencies in data. Although significant progress has been made over the last decade on approximate inference algorithms and structure learning from complete data, learning models with incomplete data remains one of machine learning’s most challenging problems. How can we model the effects of latent variables that are not directly observed?

The first part of the thesis presents two different structural conditions under which learning with latent variables is computationally tractable. The first is the "anchored'' condition, where every latent variable has at least one child that is not shared by any other parent. The second is the "singly-coupled'' condition, where every latent variable is connected to at least three children that satisfy conditional independence (possibly after transforming the data).

Variables that satisfy these conditions can be specified by an expert without requiring that the entire structure or its parameters be specified, allowing for effective use of human expertise and making room for statistical learning to do some of the heavy lifting. For both the anchored and singly-coupled conditions, practical algorithms are presented.

The second part of the thesis describes real-life applications using the anchored condition for electronic phenotyping. A human-in-the-loop learning system and a functioning emergency informatics system for real-time extraction of important clinical variables are described and evaluated.

The algorithms and discussion presented here were developed for the purpose of improving healthcare, but are much more widely applicable, dealing with the very basic questions of identifiability and learning models with latent variables - a problem that lies at the very heart of the natural and social sciences.

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Han, Kun. "Supervised Speech Separation And Processing." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1407865723.

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VANCE, DANNY W. "AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1162335608.

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Whitaker, Lisa. "Employee Satisfaction with Supervisor Support: The Case of Direct Care Workers in Nursing Homes." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062874/.

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The nursing home industry has been saturated for decades with culture change initiatives in an effort to improve resident quality of care. The direct care worker (DCW) is considered a critical position to achieving nursing facility quality improvements. Understanding what leads to job satisfaction for DCWs could result in improved resident care. The relationship DCWs have with their direct supervisor or upper-level manager can impact employee satisfaction. The purpose of this research is to identify factors that are associated with DCWs satisfaction with supervisor and management support. Data was obtained from 307 DCWs who were employed at 11 North Texas nursing homes. It was expected that factors affecting satisfaction with direct supervision and upper-level management would differ. In fact, the study found that the antecedents for employee satisfaction with supervisor support were participative decision-making/empowerment, age, information exchange and feedback. Furthermore, participative decision-making/empowerment, perceived competence, staffing, information exchange and feedback were found to affect direct care workers' satisfaction with manager support. In conclusion, this research provides a starting point towards a more holistic view of employee satisfaction with supervisor support by considering the preceding factors and its subsequent effects.

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Vann, Joseph Carl. "Relationships Between Job Satisfaction, Supervisor Support, and Profitability Among Quick Service Industry Employees." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3372.

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Low profit margins threaten the sustainability of quick service restaurants (QSRs). In the United States, low levels of employee job satisfaction and low employee perceptions of supervisor support decrease organizational profitability by as much as $151 million annually, depending on the size and type of organization. Guided by the 2-factor theory of motivation, the purpose of this correlational study was to examine the relationship between employee job satisfaction, employee perceptions of supervisor support, and organizational profitability. A convenience sample of employees from 86 QSR franchise locations in Houston, Texas completed the Job Satisfaction and Perceived Supervisor Support surveys. Multiple linear regression analysis and Bonferroni corrected significance calculation predicted organizational profitability (F(2, 71) = 9.20, p < .001, R2 = .206) and employee job satisfaction (ï?¢ = .577, p = .025). The effect size indicated that the regression model accounted for approximately 21% of the variance in organizational profitability. Employee perceptions of supervisor support (ï?¢ = -.140, p = .580) did not relate to any significant variation in organizational profitability. The findings may be of value to QSR business professionals developing initiatives to improve organizational profitability. Improving employees' perceptions of supervisor support to generate high levels of employee job satisfaction could affect behavioral social change to enhance the health and wellbeing of employees and the wealth and sustainability of QSR franchise locations.

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Mohasi, Mapalo. "The relationship between family-focused organisational and supervisor support and positive work-outcomes." Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/10970.

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This study examined the relationship between family-focused organisational and .supervisor support and the positive work outcomes, job satisfaction, affective commitment, continuous commitment and work-family enrichment.

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Brambeck, Maria Tove Helen, and Therese Marie Helen Savmyr. "Haunted By Change : Exploring and explaining the influence of Perceived Organizational Support and Perceived Supervisor Support on Commitment to Change." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355009.

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The purpose of the study is to explore and explain how and why perceived support can create a sense of want to, ought to and have to change. This study investigates perceived organizational support (POS) and perceived supervisor supports (PSS) influences on the dimensions of commitment to change (C2C), affective- (AC2C), continuous- (CC2C) and normative commitment to change (NC2C). To investigate the relationships, a mix-method approach is applied. Data is yield from 168 survey respondents from three subsidiaries in Southeast Asia within a multinational corporation (MNC) and through eight interviews with employees at one subsidiary. The findings reveals that POS is more important in change initiatives than PSS, indicating that POS is vital to understand the influence on employees C2C mindset. POS is identified as the glue that binds employees and change goals together into the desired mindset of AC2C. This study contributes to literature by presenting new perspectives concerning POS and PSS influences on C2C dimensions within an MNC. Adding a layer to research that has largely focused on the concepts within psychology. This study is an introduction to deeper understanding of the relationships between POS, PSS and the C2C dimensions. Signaling that further research should focus more on this context.

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Huss, Jakob. "Cross Site Product Page Classification with Supervised Machine Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189555.

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This work outlines a possible technique for identifying webpages that contain product specifications. Using support vector machines a product web page classifier was constructed and tested with various settings. The final result for this classifier ended up being 0.958 in precision and 0.796 in recall for product pages. The scores imply that the method could be considered a valid technique in real world web classification tasks if additional features and more data were made available.

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Reia, Susana da Conceição Margarido. "Trabalho e família: aliados? Recursos laborais e recursos familiares como preditores do spillover positivo trabalho-família." Master's thesis, Universidade de Évora, 2012. http://hdl.handle.net/10174/14679.

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Consistente com o movimento da psicologia positiva e com a hipótese da expansão de papéis, a investigação sobre a interface trabalho-família tem-se debruçado progressivamente sobre os benefícios da participação simultânea no domínio familiar e profissional, consubstanciados no conceito de spillover positivo trabalho-família (T-F). Com vista a promover tais benefícios, este estudo analisa possíveis antecedentes do spillover positivo - apoio laboral e familiar. Para tal, contámos com a participação de 102 colaboradores duma organização do sector público da área da saúde, os quais preencheram e devolveram os questionários distribuídos. Os resultados corroboram que o apoio familiar emocional é preditor das quatro dimensões de spillover positivo T-F e que o apoio organizacional percebido T-F é preditor do spillover positivo instrumental T→F. Ambientes familiares e profissionais apoiantes são, portanto, benéficos para os indivíduos, a nível pessoal e familiar. Estas e outras implicações são discutidas, destacando-se ainda algumas limitações e, sugestões para estudos futuros; ABSTRACT: Consistent with the positive psychology movement and the enhancement hypothesis, workfamily research has increasingly focused on the benefits of simultaneous participation in the work and family domain, embodied in the concept of W-F positive spillover. To promote such benefits, this study examines possible antecedents of positive spillover - work and family support. To this end, we had the advantage of 102 participants who were employees of public sector organization in the health field, who filled and returned the questionnaires. The results confirm that emotional family support is a predictor of the four dimensions of W-F positive spillover and the perceived organizational family support is a predictor of W→F instrumental positive spillover. Family and work supportive environments are therefore an advantage for individuals, personal and family level. These and other implications are discussed, highlighting still some limitations and suggestions for future research.

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Arnroth, Lukas, and Dennis Jonni Fiddler. "Supervised Learning Techniques : A comparison of the Random Forest and the Support Vector Machine." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274768.

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This thesis examines the performance of the support vector machine and the random forest models in the context of binary classification. The two techniques are compared and the outstanding one is used to construct a final parsimonious model. The data set consists of 33 observations and 89 biomarkers as features with no known dependent variable. The dependent variable is generated through k-means clustering, with a predefined final solution of two clusters. The training of the algorithms is performed using five-fold cross-validation repeated twenty times. The outcome of the training process reveals that the best performing versions of the models are a linear support vector machine and a random forest with six randomly selected features at each split. The final results of the comparison on the test set of these optimally tuned algorithms show that the random forest outperforms the linear kernel support vector machine. The former classifies all observations in the test set correctly whilst the latter classifies all but one correctly. Hence, a parsimonious random forest model using the top five features is constructed, which, to conclude, performs equally well on the test set compared to the original random forest model using all features.

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Armendariz, Robert Ernesto. "What are the Benefits of Supervisor Support? Are they affected by an Employee’s Race?" The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1336963052.

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Nespoli, Giuseppe. "Impact of Supervisor Support on Employee Job Satisfaction Among Fundraising Staff Within Higher Education." Thesis, Pepperdine University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10608404.

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This study examined the nature and impact of supervisor support on employee job satisfaction among fundraising staff within higher education. Sixteen fundraisers working in higher education institutions were interviewed about supervisor support for fundraiser task needs and personal needs, participant job satisfaction and supervisor impact on it, and participant intention to stay in their jobs and the field. Participants reported satisfaction with their supervisors’ task-related and personal support, high job satisfaction, and strong intentions to stay in their jobs and the field. Key mechanisms of supervisor support included aiding employee growth and development; accelerating and facilitating task completion; being caring, accessible, and communicative; and empowering employees. Doing meaningful work and attaining career achievement and growth also enhanced their job satisfaction. Factors increasing their stay intentions included their sense of achievement at work, rewards, and positive relationships. Suggestions for practice and continued research are offered.

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Mann, Anna, and Olivia Höft. "Categorization of Swedish e-mails using Supervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296558.

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Society today is becoming more digitalized, and a common way of communication is to send e-mails. Currently, the company Auranest has a filtering method for categorizing e-mails, but the method is a few years old. The filter provides a classification of valuable e-mails for jobseekers, where employers can make contact. The company wants to know if the categorization can be performed with a different method and improved. The degree project aims to investigate whether the categorization can be proceeded with higher accuracy using machine learning. Three supervised machine learning algorithms, Naïve Bayes, Support Vector Machine (SVM), and Decision Tree, have been examined, and the algorithm with the highest results has been compared with Auranest's existing filter. Accuracy, Precision, Recall, and F1 score have been used to determine which machine learning algorithm received the highest results and in comparison, with Auranest's filter. The results showed that the supervised machine learning algorithm SVM achieved the best results in all metrics. The comparison between Auranest's existing filter and SVM showed that SVM performed better in all calculated metrics, where the accuracy showed 99.5% for SVM and 93.03% for Auranest’s filter. The comparative results showed that accuracy was the only factor that received similar results. For the other metrics, there was a noticeable difference.
Dagens samhälle blir alltmer digitaliserat och ett vanligt kommunikationssätt är att skicka e-postmeddelanden. I dagsläget har företaget Auranest ett filter för att kategorisera e-postmeddelanden men filtret är några år gammalt. Användningsområdet för filtret är att sortera ut värdefulla e-postmeddelanden för arbetssökande, där kontakt kan ske från arbetsgivare. Företaget vill veta ifall kategoriseringen kan göras med en annan metod samt förbättras. Målet med examensarbetet är att undersöka ifall filtreringen kan göras med högre träffsäkerhet med hjälp av maskininlärning. Tre övervakade maskininlärningsalgoritmer, Naïve Bayes, Support Vector Machine (SVM) och Decision Tree, har granskats och algoritmen med de högsta resultaten har jämförts med Auranests befintliga filter. Träffsäkerhet, precision, känslighet och F1-poäng har använts för att avgöra vilken maskininlärningsalgoritm som gav högst resultat sinsemellan samt i jämförelse med Auranests filter. Resultatet påvisade att den övervakade maskininlärningsmetoden SVM åstadkom de främsta resultaten i samtliga mätvärden. Jämförelsen mellan Auranests befintliga filter och SVM visade att SVM presterade bättre i alla kalkylerade mätvärden, där träffsäkerheten visade 99,5% för SVM och 93,03% för Auranests filter. De jämförande resultaten visade att träffsäkerheten var den enda faktorn som gav liknande resultat. För de övriga mätvärdena var det en märkbar skillnad.

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Normand, Eric. "A Semi-Supervised Information Extraction Framework for Large Redundant Corpora." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/877.

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The vast majority of text freely available on the Internet is not available in a form that computers can understand. There have been numerous approaches to automatically extract information from human- readable sources. The most successful attempts rely on vast training sets of data. Others have succeeded in extracting restricted subsets of the available information. These approaches have limited use and require domain knowledge to be coded into the application. The current thesis proposes a novel framework for Information Extraction. From large sets of documents, the system develops statistical models of the data the user wishes to query which generally avoid the lim- itations and complexity of most Information Extractions systems. The framework uses a semi-supervised approach to minimize human input. It also eliminates the need for external Named Entity Recognition systems by relying on freely available databases. The final result is a query-answering system which extracts information from large corpora with a high degree of accuracy.

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Sawers, Andrew Campbell. "The Effects of Perceived Supervisor Support, Organisational Justice and Change Management Strategies in the Context of Organisational Restructuring." Thesis, University of Canterbury. Psychology, 2011. http://hdl.handle.net/10092/5324.

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This study sought to further our understanding of the antecedents of employee perceptions of organisational justice in the context of organisational restructuring. As such, this study focussed on the previously under-researched change management practices of support for downsizing victims and organisational communication quality, and the similarly under-researched organisational justice dimensions of interpersonal and informational justice, while also hypothesising a moderating effect of perceived supervisor support between these two sets of variables. Using an online survey, a total of 234 employees from a large New Zealand organisation in the Education sector were invited to participate in the study, with 71 volunteering to complete the online survey. The results showed no moderating effects of perceived supervisor support, but did show strong, significant main effects of victim support and communication quality on both interpersonal and informational justice. These findings highlight the importance of change management practices in maximising positive outcomes post-restructuring for the organisation and its employees.

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Hargell, Joanna. "Supervised Learning for Prediction of Tumour Mutational Burden." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291801.

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Tumour Mutational Burden is a promising biomarker to predict response to immunotherapy. In this thesis, statistical methods of supervised learning were used to predict TMB: GLM, Decision Trees and SVM. Predictions were based on data from targeted DNA sequencing, using variants found in the exonic, intronic, UTR and intergenic regions of the human DNA. This project was of an exploratory nature, performed in a pan-cancer setting. Both regression and classification were considered. The purpose was to investigate whether variants found in these regions of the DNA sequence are useful when predicting TMB. Poisson regression and Negative binomial regression were used within the framework of GLM. The results indicated deficiencies in the model assumptions and that the use of GLM for the application is questionable. The single regression tree did not yield satisfactory prediction accuracy. However, performance was improved by using variance reducing methods such as bagging and random forests. The use of boosted regression trees did not yield any significant improvement in prediction accuracy. In the classification setting, binary as well as multiple classes were considered. The distinction between classes was based on commonly used thresholds in clinical care to achieve immunotherapy. SVM and classification trees yielded high prediction accuracy for the binary case: a misclassification rate of 0.0242 and 0 respectively for the independent test set. In the multiple classification setting, bagging and random forests were implemented, yet, did not improve performance over the single classification tree. SVM produced a misclassification rate of 0.103, and the corresponding number for the single classification tree was 0.109. It was concluded that SVM and Decision trees are suitable methods for predicting TMB based on targeted gene panels. However, to obtain reliable predictions, there is a need to move from a pan-cancer setting to a diagnosis-based setting. Furthermore, parameters affecting TMB, like pre-analytical factors need to be included in the statistical analysis.
Denna uppsats undersöker tre metoder inom statistisk inlärning: GLM, Decision Trees och SVM, med avsikt att förutsäga mutationsbörda, TMB, för cancerpatienter. Metoderna har applicerats både inom regression och klassificering. Förutsägelser gjordes baserat på data från panel-baserad DNA-sekvensering som innehåller varianter från kodande, introniska UTR och intergeniska regioner av mänskligt DNA. Projektet ämnar att undersöka om varianter från dessa regioner av DNA-sekvensen kan vara användbara för att förutsäga mutationsbördan för en patient. Poisson-regression och Negativ Binomial-regression undersöktes inom GLM. Resultaten indikerade på brister i modellerna och att GLM inte är lämplig för denna tillämpning. Regressionsträden gav inte tillräckligt noggranna förutsägelser, men implementering av bagging och random forests förbättrade modellernas prestanda. Boosting förbättrade inte resultaten. Inom klassificering användes både binära klasser och multipla klasser. Avgränsningen mellan klasser baserades på kända gränser för TMB inom vården för att få immunoterapi. SVM och decision trees gav god prestanda för binär klassificering, med ett klassificeringsfel på 0.024 för SVM och 0 för decision trees. Bagging och random forests implementerades för det multipla fallet inom decision trees, men förbättrade inte prestandan. För multipla klasser gav SVM ett klassificeringnsfel på 0.103 och decision trees 0.109. Både SVM och decision trees visade sig vara lämpliga metoder för för att förutse värdet på TMB. Däremot, för att förutsägelserna ska vara tillförlitliga finns det ett behov av att göra denna typ av analys för varje enskild cancerdiagnos. Dessutom finns det ett behov av att inkludera parametrar från den bioinformatiska processen i den statistiska analysen.

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29

Geldenhuys, Ashley. "The influence of perceived supervisor support, psychological empowerment and affective commitment on turnover intention among support staff at a selected tertiary institution in the Western Cape." University of Western Cape, 2020. http://hdl.handle.net/11394/7841.

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Magister Commercii (Industrial Psychology) - MCom(IPS)
Literature on turnover intentions revealed that various factors predict employee turnover intention. For higher education, the ongoing transformation that has been taking place has posed many challenges, one of them being the recruitment and retention of staff in academia. However, there is the notion that employees who experience sufficient support and acknowledgement from their supervisors are more likely to develop a sense of empowerment, thus helping in either creating or increasing feelings of commitment which could decrease turnover intentions.

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30

Rico-Fontalvo, Florentino Antonio. "A Decision Support Model for Personalized Cancer Treatment." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5621.

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This work is motivated by the need of providing patients with a decision support system that facilitates the selection of the most appropriate treatment strategy in cancer treatment. Treatment options are currently subject to predetermined clinical pathways and medical expertise, but generally, do not consider the individual patient characteristics or preferences. Although genomic patient data are available, this information is rarely used in the clinical setting for real-life patient care. In the area of personalized medicine, the advancement in the fundamental understanding of cancer biology and clinical oncology can promote the prevention, detection, and treatment of cancer diseases. The objectives of this research are twofold. 1) To develop a patient-centered decision support model that can determine the most appropriate cancer treatment strategy based on subjective medical decision criteria, and patient's characteristics concerning the treatment options available and desired clinical outcomes; and 2) to develop a methodology to organize and analyze gene expression data and validate its accuracy as a predictive model for patient's response to radiation therapy (tumor radiosensitivity). The complexity and dimensionality of the data generated from gene expression microarrays requires advanced computational approaches. The microarray gene expression data processing and prediction model is built in four steps: response variable transformation to emphasize the lower and upper extremes (related to Radiosensitive and Radioresistant cell lines); dimensionality reduction to select candidate gene expression probesets; model development using a Random Forest algorithm; and validation of the model in two clinical cohorts for colorectal and esophagus cancer patients. Subjective human decision-making plays a significant role in defining the treatment strategy. Thus, the decision model developed in this research uses language and mechanisms suitable for human interpretation and understanding through fuzzy sets and degree of membership. This treatment selection strategy is modeled using a fuzzy logic framework to account for the subjectivity associated to the medical strategy and the patient's characteristics and preferences. The decision model considers criteria associated to survival rate, adverse events and efficacy (measured by radiosensitivity) for treatment recommendation. Finally, a sensitive analysis evaluates the impact of introducing radiosensitivity in the decision-making process. The intellectual merit of this research stems from the fact that it advances the science of decision-making by integrating concepts from the fields of artificial intelligence, medicine, biology and biostatistics to develop a decision aid approach that considers conflictive objectives and has a high practical value. The model focuses on criteria relevant to cancer treatment selection but it can be modified and extended to other scenarios beyond the healthcare environment.

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31

Gieseke, Fabian [Verfasser], and Oliver [Akademischer Betreuer] Kramer. "From supervised to unsupervised support vector machines and applications in astronomy / Fabian Gieseke. Betreuer: Oliver Kramer." Oldenburg : IBIT - Universitätsbibliothek, 2012. http://d-nb.info/1021178802/34.

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32

Méndez, José. "A Semi Supervised Support Vector Machine for a Recommender System : Applied to a real estate dataset." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176211.

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Recommender systems are widely used in e-commerce websites to improve the buying experience of the customer. In recent years, e-commerce has been quickly expanding and its growth has been accelerated during the COVID-19 pandemic, when customers and retailers were asked to keep their distance and do lockdowns. Therefore, there is an increasing demand for items and good recommendations to the users to improve their shopping experience. In this master’s thesis a recommender system for a real-estate website is built, based on Support Vector Machines (SVM). The main characteristic of the built model is that it is trained with a few labelled samples and the rest of unlabelled samples, using a semi-supervised machine learning paradigm. The model is constructed step-by-step from the simple SVM, until the semi-supervised Nested Cost-Sensitive Support Vector Machine (NCS-SVM). Then, we compare our model using four different kernel functions: gaussian, second-degree polynomial, fourth-degree polynomial, and linear. We also compare a user with strict housing requirements against a user with vague requirements. We finish with a discussion focusing principally on parameter tuning, and briefly in the model downsides and ethical considerations.

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33

Hilton, Doreen Bowen. "Effects of level of supervisory support and race of supervisor on perceptions of counseling and supervision /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487267024995774.

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34

Salmi, Steven W. "The effects of supervisor support and client challenge on novice counselors' self-efficacy, performance and anxiety /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487780865408292.

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35

Black, James Noel. "Development of a Support-Vector-Machine-based Supervised Learning Algorithm for Land Cover Classification Using Polarimetric SAR Imagery." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85391.

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Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized.
Master of Science
Land type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.

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36

Ekambaram, Rajmadhan. "Active Cleaning of Label Noise Using Support Vector Machines." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6830.

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Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the given labels or label noise affect the classifier performance, classifier complexity, class proportions, etc. It may be that a relatively small, but important class needs to have all its examples identified. Typical solutions to the label noise problem involve creating classifiers that are robust or tolerant to errors in the labels, or removing the suspected examples using machine learning algorithms. Finding the label noise examples through a manual review process is largely unexplored due to the cost and time factors involved. Nevertheless, we believe it is the only way to create a label noise free dataset. This dissertation proposes a solution exploiting the characteristics of the Support Vector Machine (SVM) classifier and the sparsity of its solution representation to identify uniform random label noise examples in a dataset. Application of this method is illustrated with problems involving two real-world large scale datasets. This dissertation also presents results for datasets that contain adversarial label noise. A simple extension of this method to a semi-supervised learning approach is also presented. The results show that most mislabels are quickly and effectively identified by the approaches developed in this dissertation.

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Craddock, Richard Cameron. "Support vector classification analysis of resting state functional connectivity fMRI." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31774.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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38

Lévy-Bencheton, Sandra. "L'influence du support du superviseur, du support et de la confiance organisationnels sur l'intention de quitter des employés." Thèse, 2004. http://hdl.handle.net/1866/1584.

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39

Blanchard, Charlotte. "Styles de soutien des superviseurs de recherche : relations avec les attitudes et le bien-être des étudiants gradués." Thèse, 2017. http://hdl.handle.net/1866/20329.

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40

Chen, Pei-Fan, and 程蓓芬. "The Impact of Human Resource Practices, Supervisor Support and Perceived Organizational Supporton Employee Performance." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/shv4ke.

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博士
國立中山大學
人力資源管理研究所
103
This present study developed and tested a model of the interaction effects of HR practices and supervisor support on employees’ in-role performance, OCBI (organizational citizenship behavior –individual) and OCBO (organizational citizenship behavior–organization). This model predicted employees’ performance would be positively impacted by the interaction of HR practices and supervisor support through perceived organizational support (POS). A survey using time interval measurement among 539 employees from a large electronics and appliance sales company in Taiwan revealed that HR practices and supervisor support made significant joint contributions to employees’ performance. In addition, POS fully mediated the interaction of HR practices and supervisor support - employee performance relationship. Implications for research and practice of these results were discussed

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41

Tsai, Yi-Hsient, and 蔡宜諴. "The Relationships among Organizational Support, Supervisor Support And Employee Attitudes to Safety." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/w6ry25.

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碩士
遠東科技大學
創新設計與創業管理研究所
107
Scholars studied the employee attitude scale in the past, but unfortunately did not verify it. To make up for this gap, this research uses the employees of the enterprises in Southern Taiwan Science-Based Industrial Park and Yongkang Industrial Park as the study objects to understand the impact the immediate supervisor support and organizational management support have on the occupational security felt by the employees. A total of 230 questionnaires were distributed in this study, of which 224 valid questionnaires were collected. This study adopts the stepwise analysis method of regression analysis to explore the cause-and-effect relationships among the variables. The results of the study show that both “institutional support” and “information feedback communication” have significant impacts on the occupational security felt by the employees. Based on the results of this study, the researcher conducted interviews with experts and scholars to verify the correctness of the research results. Respondents expressed positively on the results, which can be used as a basis of future studies in exploration of the theory and as a reference for employers in industries.

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42

Chinaei, Leila. "Active Learning with Semi-Supervised Support Vector Machines." Thesis, 2007. http://hdl.handle.net/10012/3071.

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A significant problem in many machine learning tasks is that it is time consuming and costly to gather the necessary labeled data for training the learning algorithm to a reasonable level of performance. In reality, it is often the case that a small amount of labeled data is available and that more unlabeled data could be labeled on demand at a cost. If the labeled data is obtained by a process outside of the control of the learner, then the learner is passive. If the learner picks the data to be labeled, then this becomes active learning. This has the advantage that the learner can pick data to gain specific information that will speed up the learning process. Support Vector Machines (SVMs) have many properties that make them attractive to use as a learning algorithm for many real world applications including classification tasks. Some researchers have proposed algorithms for active learning with SVMs, i.e. algorithms for choosing the next unlabeled instance to get label for. Their approach is supervised in nature since they do not consider all unlabeled instances while looking for the next instance. In this thesis, we propose three new algorithms for applying active learning for SVMs in a semi-supervised setting which takes advantage of the presence of all unlabeled points. The suggested approaches might, by reducing the number of experiments needed, yield considerable savings in costly classification problems in the cases when finding the training data for a classifier is expensive.

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43

Han, Yu-Man, and 韓玉滿. "The Relationship between Supervisor Support and Job Stress." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/30662640753093212648.

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碩士
大葉大學
事業經營研究所碩士在職專班
97
In the more and more competitive environment and narrower space for survival, a organization which want to achieve its objects must depend on directors’ support and members’ cooperation. When a director shows his support to his subordinates, it will affect their achievements, job satisfaction and productivity etc.. The purpose of this research is to understand the relations hip between directors’ support and job stress. This research adopted questionnaire survey and took employees in manufacturing, service trade, financial industry, insurance and nonprofit-seeking enterprise as objects of this study. We deleted some questionnaires which were filled out incompletely or some workers weren’t willing to answer. Finally three hundred questionnaires was retrieved and the retrieve-rate was 85.7％. This research was divided into four hypotheses and used descriptive statistics, reliability analysis, correlation analysis, independent-samples T Test, analysis of variance and multiple analysis regression as statistic methods. This research found that directors’ support have positive influences on job stress. According to the above-mentioned conclusion , this research aimed to provide suggestions for further studies in this field.

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44

Yung-HangChen and 陳永航. "Semi-Supervised Support Vector Machine for Face Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/33931986981605289521.

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碩士
國立成功大學
資訊工程學系碩博士班
98
Semi-supervised learning is a popular issue in the areas of pattern classification and machine learning. This issue is especially crucial in the application of face recognition. Traditional classifiers are trained by only using the labeled data. However, the labeled samples are often difficult, expensive, or time consuming to be collected since a lot of efforts should be involved from experienced human annotators. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with a limited amount of labeled data, to build a good classifier. We discuss the framework of Transductive Support Vector Machine (TSVM) from the perspective of the regularization strength induced by the unlabeled data. In this framework, SVM and TSVM are regarded as a learning machine without regularization and one with full regularization from the unlabeled data, respectively. Therefore, in order to supplement this framework of the regularization strength, it is necessary to introduce data-dependant partial regularization. To this end, we reformulate TSVM into a form with controllable regularization, which includes SVM and TSVM as special cases. Furthermore, we introduce a method of adaptive regularization that is based on Bayes theory and is updated according to its posterior distribution of parameters. The experiments on GT and FERET facical data sets indicate the promising results of the proposed work.

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45

Chang, Wan-Chen, and 張婉真. "The Relationships among Supervisor Perceived Organizational Support, Supervisor Responsiveness and Employee Organizational Citizenship Behavior." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/66306833255812822468.

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碩士
國立中央大學
人力資源管理研究所在職專班
104
Abstract Title: The Relationships among Supervisor Perceived Organizational Support, Supervisor Responsiveness and Employee Organizational Citizenship Behavior. Previous researches about the relationships between perceived organizational support and organizational citizenship behavior were mainly based on employee’s point of view. This study tends to discuss the “trickle - down effect”, which assumes supervisors perceived organizational support will affect his or her leadership to perform positive responsiveness to their subordinates. Moreover, when the subordinates receive positive feedback from the supervisors, they will be willing to behave organizational citizenship behavior in return. This study scrutinized 107 data collected from contact employees in the front line division of a local hotel chain in Taiwan. As a whole, the result of this study showed that: 1. Supervisor perceived organizational support had positive relation with employee organizational citizenship behavior. 2. Supervisor responsiveness had positive relation which influenced employee organizational citizenship behavior. 3. Supervisor perceived organizational support and supervisor responsiveness both reinforce employee organizational citizenship behavior. In other word, supervisor responsiveness played as a moderating effect to supervisor perceived organizational support and employee organizational citizenship behavior. Keywords: supervisor perceived organizational support, supervisor responsiveness, employee organizational citizenship behavior

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46

Yu, Hsiao-Yu, and 游曉郁. "Leadership and Subordinates' Effectiveness: Perceived Organizational Support and Perceived Supervisor Support Mediating Effect." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/97454718224602838694.

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碩士
國立中正大學
心理學所
94
Previous leadership behavior studies focus on the relationship between different type of leaderships and their performance. However, there is lack of description of the relationship between leadership and subordinates’ effectiveness. In this study, we used social exchange theory as the framework to discuss, with the interaction between leadership and employees, how employees’ perception of supervisor support (PSS) and perceived organizational support (POS) can affect their organizational commitment, organizational citizenship behavior, supervisor loyalty and job performance. We used dyad-approach questionnaires with sample consisted of 260 of supervisors and employees. In this study, used regression analysis the finding of for transformation and transaction factors, POS and PSS anticipation organizational commitment and organizational citizenship behavior variables of subordinates’ effectiveness was significant. Furthermore, in this study assumption model used structure equation modeling (SEM) fit value is good. Its contribution this study of model obtain advocate.

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47

Kang, Chia-Han, and 康佳涵. "The Influence of Supervisors’ Perceived Organizational Support and Personality on Subordinates’ Perceived Supervisor Support." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5457104%22.&searchmode=basic.

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碩士
國立中興大學
高階經理人碩士在職專班
107
In the organization, the supervisors play a very important role in interpersonal interaction. It is an important issue that the supervisor is willing to give subordinate support and care when they encounter difficulties. This study is explored the relation between supervisors’ perceived organizational support and subordinates’ perceived supervisor support which based on social exchange theory. According to the five-factor model proposed by Costa & McCrae (1985), the study is discussed three traits agreeableness, extraversion and neuroticism which the relevance of the support of supervisory organizations and the support of subordinates'' perception supervisors. The study is a case study of one company and the reach method is questionnaire survey in a company. The total number of questionnaire is 745 which the survey targets 55 supervisors and 690 subordinates. The analysis results are that there is no significant positive relationship between the supervisory organizational support and the subordinate supervisory support; the supervisory personality trait has a significant positive relationship with the supervisory supervisor and the subordinate supervisory supervisor, while the extroverted trait supervisor and the subordinate supervisory supervisor have no significant positive relationship; there is no significant negative relationship between the supervisor of neurotic traits and the support of subordinate consciousness supervisors. The analysis results can be the reference to case company when doing the supervisors promotion, the personality traits can be one of evaluation indexes. In the training process of management trainee, it is possible to strengthen the communication skills and how to care for employees in order to improve the supervisor’s leadership.

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48

Shu-YiChuang and 莊淑怡. "Similarity between Supervisor and Employee, LMX and Supervisory mentoring support." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/18881868704069909888.

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碩士
國立成功大學
企業管理學系專班
101
Living in a highly competitive environment, our career development has shifted from linear and stable to boundaryless and unpredictable. Therefore, we need to have the flexibility to adapt and learn quickly. Relationships are one of the most valuable resources for career development, and supervisory mentors are key source of stability in assisting individuals to successfully adapt to career challenges. Not all mentoring relationships work successfully, this study tried to examine the factors which affect supervisory mentoring support. This study collected data from 334 matched pairs of employees and supervisors from a variety of industries in Taiwan and investigated the relationships between the role of similarity (including perceived attitudes,conscientiousness and proactive personality), Leader-member exchange (LMX) and supervisory mentoring support. Meanwhile, this study also investigated the moderating role of supervisor’s willingness to mentor, pirior experience in mentoring relationships. The conclusions are as follows. First, there is the positive relationship between similarity and LMX. Secondly, LMX is positively associated with supervisory mentoring support. Thirdly, LMX mediates the relationship between similarity and supervisory mentoring support. Finally, supportive organizational climate positively moderates the relationship between LMX and career support/role modeling.

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49

Soulen, Sarah K. "Organizational commitment, perceived supervisor support, and performance a field study /." 2003. http://etd.utk.edu/2003/SoulenSarah.pdf.

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Thesis (M.A.)--University of Tennessee, Knoxville, 2003.
Title from title page screen (viewed Sept. 15, 2003). Thesis advisor: Eric Sundstrom. Document formatted into pages (v, 54 p. : ill.). Vita. Includes bibliographical references (p. 39-47).

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50

Hsu, Chong-Luen, and 徐崇倫. "Semi-supervised Support Vector Machine in Parallel Embedded System TK1." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/97g4qh.

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碩士
國立臺北科技大學
電機工程研究所
103
In this article, a Semi-Supervised SVM will be promoted to analyze and classify dataset in Pattern Recognition, CUDA will be implemented for decreasing cost time in the most time-consuming part of the procedure, to achieve the goal to accelerate the whole operating time. In the process, samples which were classified by SVM will qualify as samples which move to training sample set from test sample set according to the results of decision functions from classifiers, these chosen samples will pass through KNN to filter samples, then achieve the goal for increasing training sample set to update information in each times and raising accuracy. The parallelized Semi-Supervised SVM can apply to the newest parallelized embedded system from NVIDIA - Jetson Tegra K1 (TK1), and put on optimization of Unified Memory, to shorten the transmission time in parallelization and rise the efficiency.

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Dissertations / Theses on the topic 'Support du superviseur'

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