Relevant bibliographies by topics / Machine Learning as a Service

Academic literature on the topic 'Machine Learning as a Service'

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Published: 6 September 2023

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Journal articles on the topic "Machine Learning as a Service"

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Joseph Poulose, Travis, and S. Ganesh Kumar. "Service identification using k-NN machine learning." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 182. http://dx.doi.org/10.14419/ijet.v7i2.4.13035.

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Web service categorization is a daunting task since it requires semantic descriptions of those services which are not provided to the majority of those websites. The proposal of a Semantic based automated service discovery requires a request from the user that can be analyzed which then provides the user with a list of related webs services based on the request that instigated the search. The problem with these service categorizations listed in the Universal description Discovery and Integration (UDDI) is the way the information is related to one another. The relations follow a syntactic method. Semantic based service descriptions is necessary for accurate web categorization. With the help of machine learning we can also predict the user’s service request automatically based on previous searches and also select the best web service for a particular request that the user has made using a k-nearest neighbor algorithm. By doing this we can distinguish between the various types of user requests, provide services that are suitable for that particular request as well as suggest other services that might potentially suit the needs of the user.
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WANG, Xinyue, Nobutada FUJII, Toshiya KAIHARA, and Daisuke KOKURYO. "SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY." Acta Electrotechnica et Informatica 20, no. 2 (June 30, 2020): 11–18. http://dx.doi.org/10.15546/aeei-2020-0008.

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Bhoite, Sayee N., Vaishnavi D. Gadekar, Shashank V. Kapadnis, and Priyanka R. Ghuge. "Churn Prediction using Machine Learning Models." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2233–36. http://dx.doi.org/10.22214/ijraset.2023.52101.

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Abstract: The market is expanding quickly across all sectors, giving service providers access to a larger user base. Better offers have led to increased competition, creative new business ideas, and rising costs for acquiring new customers. Service providers understand how crucial it is to keep clients on-site in such a brief setup. Service providers must therefore prevent churn, a condition that occurs when a customer decides not to use a company's services any longer. This study examines the most widely used machine learning algorithms for churn prediction, not just in the banking industry but also in other businesses that place a high value on customer engagement.
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Točelovskis, Andrejs, and Artis Teilāns. "MACHINE LEARNING SERVICE FOR STUDENT REGISTRATION." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 24 (April 22, 2020): 103–9. http://dx.doi.org/10.17770/het2020.24.6759.

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Zuev, Dmitry, Alexey Kalistratov, and Andrey Zuev. "Machine Learning in IT Service Management." Procedia Computer Science 145 (2018): 675–79. http://dx.doi.org/10.1016/j.procs.2018.11.063.

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Zimal, Sudarshan, Chirag Shah, Shivam Borhude, Amit Birajdar, and Prof Shreedhar Patil. "Customer Churn Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 872–83. http://dx.doi.org/10.22214/ijraset.2023.49142.

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bstract: Rapid technology growth has affected corporate practices. With more items and services to select from, client churning has become a big challenge and threat to all firms. We offer a machine learning-based churn prediction model for a B2B subscription-based service provider. Our research aims to improve churn prediction. We employed machine learning to iteratively create and evaluate the resulting model using accuracy, precision, recall, and F1- score. The data comes from a financial administration subscription service. Since the given dataset is mostly non-churners, we analyzed SMOTE, SMOTEENN, and Random under Sampler to balance it. Our study shows that machine learning can anticipate client attrition. Ensemble learners perform better than single base learners, and a balanced training dataset should increase classifier performance.
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Yin, Hexiao. "Role of Artificial Intelligence Machine Learning in Deepening the Internet Plus Social Work Service." Mathematical Problems in Engineering 2021 (November 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/6915568.

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The traditional social work services are mainly visits which have some problems such as inconvenient information circulation, unreasonable resource allocation, and low service efficiency. To improve these problems, Internet plus is used to reform social work services and form an Internet plus social work service mode. Although this model has a very good improvement effect on social work service, with the rapid increase of the number of social work services and the rapid growth of the number of volunteers, this model has limitations in the arrangement of social work services and volunteer management. Therefore, based on this model, with the help of machine learning, the Internet plus social work service mode can be deepened by using machine learning to manage social services and volunteers. Internet plus social work service is the main problem in this paper. The Internet plus social work service mode is formed. Then, the deepening role of machine learning in Internet + social work service is discussed, and some problems in Internet plus social work service mode are improved. Internet plus social work service mode can better improve the problems in traditional social work service. The paper also uses machine learning to further optimize the mode of Internet plus social work service, which has a good application in social work service prospects.
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Kumaran, N., Purandhar Sri Sai, and Lokesh Manikanta. "Web Phishing Detection using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 11, no. 4 (March 30, 2022): 56–59. http://dx.doi.org/10.35940/ijitee.c9804.0311422.

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A web service is one of the most important Internet communications software services. Using fraudulent methods to get personal information is becoming increasingly widespread these days. However, it makes our lives easier, it leads to numerous security vulnerabilities to the Internet’s private structure. Web phishing is just one of the many security risks that web services face. Phishing assaults are usually detected by experienced users however, security is a primary concern for system users who are unaware of such situations. Phishing is the act of portraying malicious web runners as genuine web runners to obtain sensitive information from the end-user. Phishing is currently regarded as one of the most dangerous threats to web security. Vicious Web sites significantly encourage Internet criminal activity and inhibit the growth of Web services. As a result, there has been a tremendous push to build a comprehensive solution to prevent users from accessing such websites. We suggest a literacy-based strategy to categorize Web sites into three categories: benign, spam, and malicious. Our technology merely examines the Uniform Resource Locator (URL) itself, not the content of Web pages. As a result, it removes run-time stillness and the risk of drug users being exposed to cyber surfer-based vulnerabilities. When compared to a blacklisting service, our approach performs better on generality and content since it uses learning techniques.
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Liu, Ningbo. "An Empirical Study on Machine Learning for Enterprise Cloud Computing Service Management." Asian Journal of Economics, Business and Accounting 23, no. 9 (March 28, 2023): 48–57. http://dx.doi.org/10.9734/ajeba/2023/v23i9963.

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A machine learning method is proposed to aim at the problems of large data processing, complex data, and limited system resources in cloud computing service management. First, multi-department analysis is carried out on cloud service management data, and relevant management data is standardized to form a standardized management data collection; Machine learning is used to classify, mine, and extract service management data, and corresponding management measures are taken promptly to improve the management level of cloud computing services. MATLAB simulation shows that machine learning can improve the level of cloud computing service management, simplify the process of cloud computing service management, shorten the management time of cloud computing services, and meet the actual needs of service management.
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Han, Bo, and Rongli Zhang. "Virtual Machine Allocation Strategy Based on Statistical Machine Learning." Mathematical Problems in Engineering 2022 (July 5, 2022): 1–6. http://dx.doi.org/10.1155/2022/8190296.

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At present, big data cloud computing has been widely used in many enterprises, and it serves tens of millions of users. One of the core technologies of big data cloud service is computer virtualization technology. The reasonable allocation of virtual machines on available hosts is of great significance to the performance optimization of cloud computing. We know that with the continuous development of information technology and the increasing number of computer users, different virtualization technologies and the increasing number of virtual machines in the network make the effective allocation of virtualization resources more and more difficult. In order to solve and optimize this problem, we propose a virtual machine allocation algorithm based on statistical machine learning. According to the resource requirements of each virtual machine in cloud service, the corresponding comprehensive performance analysis model is established, and the reasonable virtual machine allocation algorithm description of the host in the resource pool is realized according to the virtualization technology type or mode provided by the model. Experiments show that this method has the advantages of overall performance, load balancing, and supporting different types of virtualization.
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Dissertations / Theses on the topic "Machine Learning as a Service"

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Hesamifard, Ehsan. "Privacy Preserving Machine Learning as a Service." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703277/.

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Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.
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Altalabani, Osama. "An automatic machine-learning framework for testing service-oriented architecure." Thesis, Kingston University, 2014. http://eprints.kingston.ac.uk/32198/.

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Today, Service Oriented Architecture (SOA) systems such as web services have the advantage of offering defined protocol and standard requirement specifications by means of a formal contract between the service requestor and the service provider, for example, the WSDL (Web Services Description Language) , PBEL (Business Process Execution Language), and BPMN (Business Process Model and Notation). This gives a high degree of flexibility to the design, development, Information Technology (IT) infrastructure implementation, and promise a world where computing resources work transparently and efficiently. Furthermore, the rich interface standards and specifications of SOA web services (collectively referred to as the WS-* Architecture) enable service providers and consumers to solve important problems, as these interfaces enable the development of interoperable computing environments that incorporate end-to-end security, reliability and transaction support, thus, promoting existing IT infrastructure investments. However, many of the benefits of SOA become challenges for testing approaches and frameworks due to their specific design and implementation characteristics, which cause many testability problems. Thus, a number of testing approaches and frameworks have been proposed in the literature to address various aspects of SOA testability. However, most of these approaches and frameworks are based on intuition and not carried out in a systematic manner that is based on the standards and specifications of SOA. Generally, they lack sophisticated and automated testing, which provide data mining and knowledge discovery in accordance with the system based on SOA requirements, which consequently would provide better testability, deeper intelligence and prudence. Thus, this thesis proposes an automated and systematic testing framework based on user requirements, both functional and non-functional, with support of machine-learning techniques for intelligent reliability, real-time monitoring, SOA protocols and standard requirements coverage analysis to improve the testability of SOA-based systems. This thesis addresses the development, implementation, and evaluation of the proposed framework, by means of a proof-of-concept prototype for testing SOA systems based on the web services protocol stack specifications. The framework extends to intelligent analysis of SOA web service specifications and the generation of test cases based on static test analysis using machine-learning support.
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MUSCI, MARIA ANGELA. "Service robotics and machine learning for close-range remote sensing." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2903488.

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MAZZIA, VITTORIO. "Machine Learning Algorithms and their Embedded Implementation for Service Robotics Applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968456.

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Ashfaq, Awais. "Predicting clinical outcomes via machine learning on electronic health records." Licentiate thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39309.

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The rising complexity in healthcare, exacerbated by an ageing population, results in ineffective decision-making leading to detrimental effects on care quality and escalates care costs. Consequently, there is a need for smart decision support systems that can empower clinician's to make better informed care decisions. Decisions, which are not only based on general clinical knowledge and personal experience, but also rest on personalised and precise insights about future patient outcomes. A promising approach is to leverage the ongoing digitization of healthcare that generates unprecedented amounts of clinical data stored in Electronic Health Records (EHRs) and couple it with modern Machine Learning (ML) toolset for clinical decision support, and simultaneously, expand the evidence base of medicine. As promising as it sounds, assimilating complete clinical data that provides a rich perspective of the patient's health state comes with a multitude of data-science challenges that impede efficient learning of ML models. This thesis primarily focuses on learning comprehensive patient representations from EHRs. The key challenges of heterogeneity and temporality in EHR data are addressed using human-derived features appended to contextual embeddings of clinical concepts and Long-Short-Term-Memory networks, respectively. The developed models are empirically evaluated in the context of predicting adverse clinical outcomes such as mortality or hospital readmissions. We also present evidence that, surprisingly, different ML models primarily designed for non-EHR analysis (like language processing and time-series prediction) can be combined and adapted into a single framework to efficiently represent EHR data and predict patient outcomes.
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Dhekne, Rucha P. "Machine Learning Techniques to Provide Quality of Service in Cognitive Radio Technology." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258579803.

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Blank, Clas, and Tomas Hermansson. "A Machine Learning approach to churn prediction in a subscription-based service." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240397.

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Prenumerationstjänster blir alltmer populära i dagens samhälle. En av nycklarna för att lyckas med en prenumerationsbaserad affärsmodell är att minimera kundbortfall (eng. churn), dvs. kunder som avslutar sin prenumeration inom en viss tidsperiod. I och med den ökande digitaliseringen, är det nu enklare att samla in data än någonsin tidigare. Samtidigt växer maskininlärning snabbt och blir alltmer lättillgängligt, vilket möjliggör nya infallsvinklar på problemlösning. Denna rapport kommer testa och utvärdera ett försök att förutsäga kundbortfall med hjälp av maskininlärning, baserat på kunddata från ett företag med en prenumerationsbaserad affärsmodell där prenumeranten får besöka live-event till en fast månadskostnad. De maskininlärningsmodeller som användes i testerna var Random Forests, Support Vector Machines, Logistic Regression, och Neural Networks som alla tränades med användardata från företaget. Modellerna gav ett slutligt träffsäkerhetsresultat i spannet mellan 73,7 % och 76,7 %. Därutöver tenderade modellerna att ge ett högre resultat för precision och täckning gällande att klassificera kunder som sagt upp sin prenumeration än för de som fortfarande var aktiva. Dessutom kunde det konstateras att de kundegenskaper som hade störst inverkan på klassifikationen var ”Använda Biljetter” och ”Längd på Prenumeration”. Slutligen kommer det i denna rapport diskuteras hur informationen angående vilka kunder som sannolikt kommer avsluta sin prenumeration kan användas ur ett mer affärsmässigt perspektiv.
In today’s world subscription-based online services are becoming increasingly popular. One of the keys to success in a subscription-based business model is to minimize churn, i.e. customer canceling their subscriptions. Due to the digitalization of the world, data is easier to collect than ever before. At the same time machine learning is growing and is made more available. That opens up new possibilities to solve different problems with the use of machine learning. This paper will test and evaluate a machine learning approach to churn prediction, based on the user data from a company with an online subscription service letting the user attend live shows to a fixed price. To perform the tests different machine learning models were used, both individually and combined. The models were Random Forests, Support Vector Machines, Logistic Regression and Neural Networks. In order to train them a data set containing either active or churned users was provided. Eventually the models returned accuracy results ranging from 73.7 % to 76.7 % when classifying churners based on their activity data. Furthermore, the models turned out to have higher scores for precision and recall for classifying the churners than the non-churners. In addition, the features that had the most impact on the model regarding the classification were Tickets Used and Length of Subscription. Moreover, this paper will discuss how churn prediction can be used from a business perspective.
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Hill, Jerry L., and Randall P. Mora. "An Autonomous Machine Learning Approach for Global Terrorist Recognition." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581675.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California
A major intelligence challenge we face in today's national security environment is the threat of terrorist attack against our national assets, especially our citizens. This paper addresses global reconnaissance which incorporates an autonomous Intelligent Agent/Data Fusion solution for recognizing potential risk of terrorist attack through identifying and reporting imminent persona-oriented terrorist threats based on data reduction/compression of a large volume of low latency data possibly from hundreds, or even thousands of data points.
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Darborg, Alex. "Real-time face recognition using one-shot learning : A deep learning and machine learning project." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40069.

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Face recognition is often described as the process of identifying and verifying people in a photograph by their face. Researchers have recently given this field increased attention, continuously improving the underlying models. The objective of this study is to implement a real-time face recognition system using one-shot learning. “One shot” means learning from one or few training samples. This paper evaluates different methods to solve this problem. Convolutional neural networks are known to require large datasets to reach an acceptable accuracy. This project proposes a method to solve this problem by reducing the number of training instances to one and still achieving an accuracy close to 100%, utilizing the concept of transfer learning.
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Tataru, Augustin. "Metrics for Evaluating Machine Learning Cloud Services." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-37882.

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Machine Learning (ML) is nowadays being offered as a service by several cloud providers. Consumers require metrics to be able to evaluate and compare between multiple ML cloud services. There aren’t many established metrics that can be used specifically for these types of services. In this paper, the Goal-QuestionMetric paradigm is used to define a set of metrics applicable for ML cloud services. The metrics are created based on goals expressed by professionals who use or are interested in using these services. At the end, a questionnaire is used to evaluate the metrics based on two criteria: relevance and ease of use.
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Books on the topic "Machine Learning as a Service"

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Ebers, Martin, and Paloma Krõõt Tupay. Artificial Intelligence and Machine Learning Powered Public Service Delivery in Estonia. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19667-6.

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Kumar, Sumit, Raj Setia, and Kuldeep Singh, eds. Artificial Intelligence and Machine Learning in Satellite Data Processing and Services. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7698-8.

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Zhou, Zhi-Hua. Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.

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Jung, Alexander. Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.

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Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski. Machine Learning. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.

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Fernandes de Mello, Rodrigo, and Moacir Antonelli Ponti. Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.

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Bell, Jason. Machine Learning. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.

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Huang, Kaizhu, Haiqin Yang, Irwin King, and Michael Lyu. Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.

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Jebara, Tony. Machine Learning. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.

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Elger, Peter Peter, and Eoin Eoin Shanaghy. AI As a Service: Serverless Machine Learning with AWS. Manning Publications Company, 2020.

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Book chapters on the topic "Machine Learning as a Service"

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Basler, Daniel. "Machine Learning as a Service." In Neuronale Netze mit C# programmieren, 237–72. München: Carl Hanser Verlag GmbH & Co. KG, 2021. http://dx.doi.org/10.3139/9783446464261.009.

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Obaid, Abdulrahman Mohammed Hussein, Santosh Kumar Pani, and Prasant Kumar Pattnaik. "A Critical Analysis of Brokering Services (Scheduling as Service)." In Machine Learning and Information Processing, 425–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3_39.

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Olivotti, Daniel, Jens Passlick, Alexander Axjonow, Dennis Eilers, and Michael H. Breitner. "Combining Machine Learning and Domain Experience: A Hybrid-Learning Monitor Approach for Industrial Machines." In Exploring Service Science, 261–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00713-3_20.

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Leros, Apostolos P., and Antonios S. Andreatos. "Network Traffic Analytics for Internet Service Providers—Application in Early Prediction of DDoS Attacks." In Machine Learning Paradigms, 233–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94030-4_10.

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Baresi, Luciano, and Giovanni Quattrocchi. "Training and Serving Machine Learning Models at Scale." In Service-Oriented Computing, 669–83. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20984-0_48.

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Li, Siqiao, Qingchen Wang, and Ger Koole. "Predicting Call Center Performance with Machine Learning." In Advances in Service Science, 193–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04726-9_19.

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Messer, Uwe, and Stefan Faußer. "Machine Learning Infusion in Service Processes." In Automatisierung und Personalisierung von Dienstleistungen, 343–64. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30168-2_14.

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Idé, Tsuyoshi. "Formalizing Expert Knowledge Through Machine Learning." In Global Perspectives on Service Science: Japan, 157–75. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3594-9_11.

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Price, Ed, Adnan Masood, and Gaurav Aroraa. "Azure Machine Learning." In Hands-on Azure Cognitive Services, 321–54. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7249-7_10.

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Danninger, Maria, Erica Robles, Leila Takayama, QianYing Wang, Tobias Kluge, Rainer Stiefelhagen, and Clifford Nass. "The Connector Service-Predicting Availability in Mobile Contexts." In Machine Learning for Multimodal Interaction, 129–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11965152_12.

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Conference papers on the topic "Machine Learning as a Service"

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Philipp, Robert, Andreas Mladenow, Christine Strauss, and Alexander Völz. "Machine Learning as a Service." In iiWAS '20: The 22nd International Conference on Information Integration and Web-based Applications & Services. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3428757.3429152.

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Li Luo, Hangjiang Liu, Xiaolong Hou, and Yingkang Shi. "Machine learning methods for surgery cancellation." In 2016 13th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2016. http://dx.doi.org/10.1109/icsssm.2016.7538652.

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Wei Shi and Yan-Pingchen. "Web service selection and classification of service requesters." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890756.

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Zhu, Chaochao. "How long will the Service Time in a Ride-Hailing Service?" In MLMI '20: 2020 The 3rd International Conference on Machine Learning and Machine Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3426826.3426828.

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Ribeiro, Mauro, Katarina Grolinger, and Miriam A. M. Capretz. "MLaaS: Machine Learning as a Service." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.152.

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Liu, Wei, Zong-Tian Liu, and Wei-Qin Tong. "Agent-Oriented Modeling for Grid Service." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370119.

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Yelin Li, Junjie Wu, and Hui Bu. "When quantitative trading meets machine learning: A pilot survey." In 2016 13th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2016. http://dx.doi.org/10.1109/icsssm.2016.7538632.

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Ma, Shang-Pin, Jong-Yih Kuo, Yong-Yi Fanjiang, Chin-Pin Tung, and Chun-Ying Huang. "Optimal service selection for composition based on weighted service flow and Genetic Algorithm." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580692.

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Dong-Dai Zhou, Zhuo Zhang, Shao-Chun Zhong, Yi Yao, and Hong-Mei Zhang. "The study of service oriented architecture of E-learning resources and personalized service model." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212810.

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Spreadsheets, Using, Chi-Yurl Yoon, and Shin-Gak Kang. "A study on machine learning web service." In 2017 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2017. http://dx.doi.org/10.1109/ictc.2017.8190774.

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Reports on the topic "Machine Learning as a Service"

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Duarte, Javier, and et al. FPGAs as a Service to Accelerate Machine Learning Inference. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1570210.

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Duarte, Javier, and et al. Accelerated Machine Learning as a Service for Particle Physics Computing. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1592124.

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Lohn, Andrew. Hacking AI: A Primer for Policymakers on Machine Learning Cybersecurity. Center for Security and Emerging Technology, December 2020. http://dx.doi.org/10.51593/2020ca006.

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Machine learning systems’ vulnerabilities are pervasive. Hackers and adversaries can easily exploit them. As such, managing the risks is too large a task for the technology community to handle alone. In this primer, Andrew Lohn writes that policymakers must understand the threats well enough to assess the dangers that the United States, its military and intelligence services, and its civilians face when they use machine learning.
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Cilliers, Jacobus, Eric Dunford, and James Habyarimana. What Do Local Government Education Managers Do to Boost Learning Outcomes? Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/064.

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Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of performance are: i) the frequency of school visits; ii) school and teacher incentives administered by the district manager; and iii) performance review of staff. Although the model is not causal, these findings suggest the importance of robust systems to motivate district staff, schools, and teachers, that include frequent monitoring of schools. They also show the importance of surveying subordinates of managers, in order to produce richer information on management practices.
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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283386.

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Chase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada223732.

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Kagie, Matthew J., and Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1561828.

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Lin, Youzuo, Shihang Feng, and Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1876777.

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Caplin, Andrew, Daniel Martin, and Philip Marx. Modeling Machine Learning. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30600.

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