Literatura académica sobre el tema "Machine learning tools"
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Artículos de revistas sobre el tema "Machine learning tools"
Pagadipala srikanth, Pulimamidi sai teja, Borigam Lakshmi prasad y Veduruvada pavan kalyan. "A comprehensive review of machine learning techniques in computer numerical controlled machines". International Journal of Science and Research Archive 9, n.º 1 (30 de junio de 2023): 627–37. http://dx.doi.org/10.30574/ijsra.2023.9.1.0491.
Texto completoHussein, Eslam A., Christopher Thron, Mehrdad Ghaziasgar, Antoine Bagula y Mattia Vaccari. "Groundwater Prediction Using Machine-Learning Tools". Algorithms 13, n.º 11 (17 de noviembre de 2020): 300. http://dx.doi.org/10.3390/a13110300.
Texto completoBosetti, Paolo, Matteo Ragni y Matteo Leoni. "Modern machine-learning tools for crystallography". Acta Crystallographica Section A Foundations and Advances 73, a2 (1 de diciembre de 2017): C562. http://dx.doi.org/10.1107/s2053273317090118.
Texto completoPadarian, José, Budiman Minasny y Alex B. McBratney. "Machine learning and soil sciences: a review aided by machine learning tools". SOIL 6, n.º 1 (6 de febrero de 2020): 35–52. http://dx.doi.org/10.5194/soil-6-35-2020.
Texto completoMahardika, Rizka. "THE USE OF TRANSLATION TOOL IN EFL LEARNING: DO MACHINE TRANSLATION GIVE POSITIVE IMPACT IN LANGUAGE LEARNING?" Pedagogy : Journal of English Language Teaching 5, n.º 1 (30 de julio de 2017): 49. http://dx.doi.org/10.32332/pedagogy.v5i1.755.
Texto completoO’Gorman, Eoin J. "Machine learning ecological networks". Science 377, n.º 6609 (26 de agosto de 2022): 918–19. http://dx.doi.org/10.1126/science.add7563.
Texto completoNg, Wenfa. "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization". Biotechnology and Bioprocessing 2, n.º 9 (2 de noviembre de 2021): 01–07. http://dx.doi.org/10.31579/2766-2314/060.
Texto completoSukhoparov, M. E., K. I. Salakhutdinova y I. S. Lebedev. "Software Identification by Standard Machine Learning Tools". Automatic Control and Computer Sciences 55, n.º 8 (diciembre de 2021): 1175–79. http://dx.doi.org/10.3103/s0146411621080459.
Texto completoGleyzer, S. V., L. Moneta y Omar A. Zapata. "Development of Machine Learning Tools in ROOT". Journal of Physics: Conference Series 762 (octubre de 2016): 012043. http://dx.doi.org/10.1088/1742-6596/762/1/012043.
Texto completoYousefi, Jamileh y Andrew Hamilton-Wright. "Characterizing EMG data using machine-learning tools". Computers in Biology and Medicine 51 (agosto de 2014): 1–13. http://dx.doi.org/10.1016/j.compbiomed.2014.04.018.
Texto completoTesis sobre el tema "Machine learning tools"
Kanwar, John. "Smart cropping tools with help of machine learning". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74827.
Texto completoMaskinlärning har funnits en lång tid. Deras jobb varierar från flera olika ämnen. Allting från självkörande bilar till data mining. När en person tar en bild med en mobiltelefon händer det lätt att bilden är lite sned. Det händer också att en tar spontana bilder med sin mobil, vilket kan leda till att det kommer med något i kanten av bilden som inte bör vara där. Det här examensarbetet kombinerar maskinlärning med fotoredigeringsverktyg. Det kommer att utforska möjligheterna hur maskinlärning kan användas för att automatiskt beskära bilder estetsikt tilltalande samt hur maskinlärning kan användas för att skapa ett porträttbeskärningsverktyg. Det kommer även att gå igenom hur en räta-till-funktion kan bli implementerad med hjälp av maskinlärning. Till sist kommer det att jämföra dessa verktyg med andra programs automatiska beskärningsverktyg.
Nordin, Alexander Friedrich. "End to end machine learning workflow using automation tools". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119776.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 79-80).
We have developed an open source library named Trane and integrated it with two open source libraries to build an end-to-end machine learning workflow that can facilitate rapid development of machine learning models. The three components of this workflow are Trane, Featuretools and ATM. Trane enumerates tens of prediction problems relevant to any dataset using the meta information about the data. Furthermore, Trane generates training examples required for training machine learning models. Featuretools is an open-source software for automatically generating features from a dataset. Auto Tune Models (ATM), an open source library, performs a high throughput search over modeling options to find the best modeling technique for a problem. We show the capability of these three tools and highlight the open-source development of Trane.
by Alexander Friedrich Nordin.
M. Eng.
Jalali, Mana. "Voltage Regulation of Smart Grids using Machine Learning Tools". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/95962.
Texto completoWith advent of renewable energies into the power systems, innovative and automatic monitoring and control techniques are required. More specifically, voltage regulation for distribution grids with solar generation is a can be a challenging task. Moreover, due to frequency and intensity of the voltage changes, traditional utility-owned voltage regulation equipment are not useful in long term. On the other hand, smart inverters installed with solar panels can be used for regulating the voltage. Smart inverters can be programmed to inject or absorb reactive power which directly influences the voltage. Utility can monitor, control and sync the inverters across the grid to maintain the voltage within the desired limits. Machine learning and optimization techniques can be applied for automation of voltage regulation in smart grids using the smart inverters installed with solar panels. In this work, voltage regulation is addressed by reactive power control.
Viswanathan, Srinidhi. "ModelDB : tools for machine learning model management and prediction storage". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113540.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 99-100).
Building a machine learning model is often an iterative process. Data scientists train hundreds of models before finding a model that meets acceptable criteria. But tracking these models and remembering the insights obtained from them is an arduous task. In this thesis, we present two main systems for facilitating better tracking, analysis, and querying of scikit-learn machine learning models. First, we introduce our scikit-learn client for ModelDB, a novel end-to-end system for managing machine learning models. The client allows data scientists to easily track diverse scikit-learn workflows with minimal changes to their code. Then, we describe our extension to ModelDB, PredictionStore. While the ModelDB client enables users to track the different models they have run, PredictionStore creates a prediction matrix to tackle the remaining piece in the puzzle: facilitating better exploration and analysis of model performance. We implement a query API to assist in analyzing predictions and answering nuanced questions about models. We also implement a variety of algorithms to recommend particular models to ensemble utilizing the prediction matrix. We evaluate ModelDB and PredictionStore on different datasets and determine ModelDB successfully tracks scikit-learn models, and most complex model queries can be executed in a matter of seconds using our query API. In addition, the workflows demonstrate significant improvement in accuracy using the ensemble algorithms. The overall goal of this research is to provide a flexible framework for training scikit-learn models, storing their predictions/ models, and efficiently exploring and analyzing the results.
by Srinidhi Viswanathan.
M. Eng.
Borodavkina, Lyudmila 1977. "Investigation of machine learning tools for document clustering and classification". Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8932.
Texto completoIncludes bibliographical references (leaves 57-59).
Data clustering is a problem of discovering the underlying data structure without any prior information about the data. The focus of this thesis is to evaluate a few of the modern clustering algorithms in order to determine their performance in adverse conditions. Synthetic Data Generation software is presented as a useful tool both for generating test data and for investigating results of the data clustering. Several theoretical models and their behavior are discussed, and, as the result of analysis of a large number of quantitative tests, we come up with a set of heuristics that describe the quality of clustering output in different adverse conditions.
by Lyudmila Borodavkina.
M.Eng.
Song, Qi. "Developing machine learning tools to understand transcriptional regulation in plants". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93512.
Texto completoDoctor of Philosophy
Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance to plants. Genomics technology has been used in past decade to generate gene expression data under different abiotic stresses for the model plant, Arabidopsis. Recent new genomic technologies, such as DAP-seq, have generated large scale regulatory maps that provide information regarding which gene has the potential to regulate other genes in the genome. However, this technology does not provide context specific interactions. It is unknown which transcription factor can regulate which gene under a specific abiotic stress condition. To address this challenge, several computational tools were developed to identify regulatory interactions and co-regulating genes for stress response. In addition, using single cell RNA-seq data generated from the model plant organism Arabidopsis, preliminary analysis was performed to build model that classifies Arabidopsis root cell types. This analysis is the first step towards the ultimate goal of constructing cell-typespecific regulatory network for Arabidopsis, which is important for improving current understanding of stress response in plants.
Nagler, Dylan Jeremy. "SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder". Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:12705172.
Texto completoParikh, Neena (Neena S. ). "Interactive tools for fantasy football analytics and predictions using machine learning". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100687.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 83-84).
The focus of this project is multifaceted: we aim to construct robust predictive models to project the performance of individual football players, and we plan to integrate these projections into a web-based application for in-depth fantasy football analytics. Most existing statistical tools for the NFL are limited to the use of macro-level data; this research looks to explore statistics at a finer granularity. We explore various machine learning techniques to develop predictive models for different player positions including quarterbacks, running backs, wide receivers, tight ends, and kickers. We also develop an interactive interface that will assist fantasy football participants in making informed decisions when managing their fantasy teams. We hope that this research will not only result in a well-received and widely used application, but also help pave the way for a transformation in the field of football analytics.
by Neena Parikh.
M. Eng.
Arango, Argoty Gustavo Alonso. "Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88987.
Texto completoDoctor of Philosophy
Antimicrobial resistance (AMR) is one of the biggest threats to human public health. It has been estimated that the number of deaths caused by AMR will surpass the ones caused by cancer on 2050. The seriousness of these projections requires urgent actions to understand and control the spread of AMR. In the last few years, metagenomics has stand out as a reliable tool for the analysis of the microbial diversity and the AMR. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics, a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. In particular, by the development of computational pipelines to process metagenomics data in the cloud and distributed systems, the development of machine learning and deep learning tools to ease the computational cost of detecting antibiotic resistance genes in metagenomic data, and the integration of crowdsourcing as a way to curate and validate antibiotic resistance genes.
Schildt, Alexandra y Jenny Luo. "Tools and Methods for Companies to Build Transparent and Fair Machine Learning Systems". Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279659.
Texto completoAI har snabbt vuxit från att vara ett vagt koncept till en ny teknik som många företag vill eller är i färd med att implementera. Forskare och organisationer är överens om att AI och utvecklingen inom maskininlärning har enorma potentiella fördelar. Samtidigt finns det en ökande oro för att utformningen och implementeringen av AI-system inte tar de etiska riskerna i beaktning. Detta har triggat en debatt kring vilka principer och värderingar som bör vägleda AI i dess utveckling och användning. Det saknas enighet kring vilka värderingar och principer som bör vägleda AI-utvecklingen, men också kring vilka praktiska verktyg som skall användas för att implementera dessa principer i praktiken. Trots att forskare, organisationer och myndigheter har föreslagit verktyg och strategier för att arbeta med etiskt AI inom organisationer, saknas ett helhetsperspektiv som binder samman de verktyg och strategier som föreslås i etiska, tekniska och organisatoriska diskurser. Rapporten syftar till överbrygga detta gap med följande syfte: att utforska och presentera olika verktyg och metoder som företag och organisationer bör ha för att bygga maskininlärningsapplikationer på ett rättvist och transparent sätt. Studien är av kvalitativ karaktär och datainsamlingen genomfördes genom en litteraturstudie och intervjuer med ämnesexperter från forskning och näringsliv. I våra resultat presenteras ett antal verktyg och metoder för att öka rättvisa och transparens i maskininlärningssystem. Våra resultat visar också att företag bör arbeta med en kombination av verktyg och metoder, både utanför och inuti utvecklingsprocessen men också i olika stadier i utvecklingsprocessen. Verktyg utanför utvecklingsprocessen så som etiska riktlinjer, utsedda roller, workshops och utbildningar har positiva effekter på engagemang och kunskap samtidigt som de ger värdefulla möjligheter till förbättringar. Dessutom indikerar resultaten att det är kritiskt att principer på hög nivå översätts till mätbara kravspecifikationer. Vi föreslår ett antal verktyg i pre-model, in-model och post-model som företag och organisationer kan implementera för att öka rättvisa och transparens i sina maskininlärningssystem.
Libros sobre el tema "Machine learning tools"
Khosrowpour, Mehdi y Information Resources Management Association. Machine learning: Concepts, methodologies, tools and applications. Hershey, PA: Information Science Reference, 2012.
Buscar texto completoLearning computer numerical control. Albany, NY: Delmar Publishers, 1992.
Buscar texto completoCost-sensitive machine learning. Boca Raton, FL: CRC Press, 2012.
Buscar texto completoEibe, Frank y Hall Mark A, eds. Data mining: Practical machine learning tools and techniques. 3a ed. Burlington, MA: Morgan Kaufmann, 2011.
Buscar texto completoCastiello, Maria Elena. Computational and Machine Learning Tools for Archaeological Site Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88567-0.
Texto completoMachine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.
Buscar texto completoPardalos, Panos M., Stamatina Th Rassia y Arsenios Tsokas, eds. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84459-2.
Texto completoWitten, I. H. Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, Calif: Morgan Kaufmann, 2000.
Buscar texto completoSrinivasa, K. G., G. M. Siddesh y S. R. Manisekhar, eds. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5.
Texto completoNational Institute of Standards and Technology (U.S.), ed. Manufacturing technology learning modules: Sharing resources for school outreach. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.
Buscar texto completoCapítulos de libros sobre el tema "Machine learning tools"
Geetha, T. V. y S. Sendhilkumar. "Machine Learning: Tools and Software". En Machine Learning, 103–25. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-5.
Texto completoSekar, Maris. "Storytelling Tools". En Machine Learning for Auditors, 173–79. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8051-5_18.
Texto completoAlvo, Mayer. "Tools for Machine Learning". En Statistical Inference and Machine Learning for Big Data, 277–327. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06784-6_11.
Texto completoVasudevan, Shriram K., Nitin Vamsi Dantu, Sini Raj Pulari y T. S. Murugesh. "Tools and Pre-requisites". En Machine Learning with oneAPI, 56–75. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003393122-5.
Texto completoVasudevan, Shriram K., Nitin Vamsi Dantu, Sini Raj Pulari y T. S. Murugesh. "More Intel Tools for Enhanced Development Experience". En Machine Learning with oneAPI, 181–92. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003393122-12.
Texto completoScutari, Marco y Mauro Malvestio. "Tools for Developing Pipelines". En The Pragmatic Programmer for Machine Learning, 247–62. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429292835-10.
Texto completoLi, Xuekui, Lei Chen, Yi Shi y Ping Cui. "Learning Parameter Analysis for Machine Reading Comprehension". En Simulation Tools and Techniques, 485–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72792-5_39.
Texto completoEtaati, Leila. "Overview of Microsoft Machine Learning Tools". En Machine Learning with Microsoft Technologies, 355–58. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3658-1_20.
Texto completoSmith, Einar. "Scientific Machine Learning with PyTorch". En Introduction to the Tools of Scientific Computing, 359–410. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16972-4_16.
Texto completoEtaati, Leila. "Deep Learning Tools with Cognitive Toolkit (CNTK)". En Machine Learning with Microsoft Technologies, 287–302. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3658-1_17.
Texto completoActas de conferencias sobre el tema "Machine learning tools"
Raboudi, Khaoula y Abdelkader Ben Saci. "Machine learning for optimized buildings morphosis". En DTUC '20: Digital Tools & Uses Congress. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3423603.3424057.
Texto completoKaytan, Mustafa y Ibrahim Berkan Aydilek. "A review on machine learning tools". En 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090257.
Texto completoOwen, Steven. "Working with CUBIT's Machine Learning Tools." En Proposed for presentation at the Cubit 200 virtual class held October 19-20, 2021 in online, . US DOE, 2021. http://dx.doi.org/10.2172/1892152.
Texto completoAgarwal, Namita y Saikat Das. "Interpretable Machine Learning Tools: A Survey". En 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308260.
Texto completoAbid, Saad Bin, Vishal Mahajan y Levi Lucio. "Machine Learning for Learnability of MDD tools". En The 31st International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2019. http://dx.doi.org/10.18293/seke2019-050.
Texto completoTAGLIAFERRI, ROBERTO, FRANCESCO IORIO, FRANCESCO NAPOLITANO, GIANCARLO RAICONI y GENNARO MIELE. "INTERACTIVE MACHINE LEARNING TOOLS FOR DATA ANALYSIS". En Proceedings of the 6th International Workshop on Data Analysis in Astronomy “Livio Scarsi”. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812779458_0030.
Texto completoRamírez-Amaro, K. y J. C. Chimal-Eguía. "Machine Learning Tools to Time Series Forecasting". En 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session MICAI. IEEE, 2007. http://dx.doi.org/10.1109/micai.2007.42.
Texto completoObulesu, O., M. Mahendra y M. ThrilokReddy. "Machine Learning Techniques and Tools: A Survey". En 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2018. http://dx.doi.org/10.1109/icirca.2018.8597302.
Texto completoSworna, Zarrin Tasnim, Anjitha Sreekumar, Chadni Islam y Muhammad Ali Babar. "Security Tools’ API Recommendation Using Machine Learning". En 18th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011708300003464.
Texto completoKlumbytė, Goda, Claude Draude y Alex Taylor. "Critical Tools for Machine Learning: Situating, Figuring, Diffracting, Fabulating Machine Learning Systems Design". En CHItaly '21: 14th Biannual Conference of the Italian SIGCHI Chapter. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3464385.3467475.
Texto completoInformes sobre el tema "Machine learning tools"
Harris, Philip. Physics Community Needs, Tools, and Resources for Machine Learning. Office of Scientific and Technical Information (OSTI), marzo de 2022. http://dx.doi.org/10.2172/1873720.
Texto completoTeodoridis, Florenta, Jino Lu y Jeffrey Furman. Mapping the Knowledge Space: Exploiting Unassisted Machine Learning Tools. Cambridge, MA: National Bureau of Economic Research, octubre de 2022. http://dx.doi.org/10.3386/w30603.
Texto completoCary, Dakota y Daniel Cebul. Destructive Cyber Operations and Machine Learning. Center for Security and Emerging Technology, noviembre de 2020. http://dx.doi.org/10.51593/2020ca003.
Texto completoCao, Larry. I. Machine Learning and Data Science Applications in Investments. CFA Institute Research Foundation, marzo de 2023. http://dx.doi.org/10.56227/23.1.7.
Texto completoRudner, Tim y Helen Toner. Key Concepts in AI Safety: Interpretability in Machine Learning. Center for Security and Emerging Technology, marzo de 2021. http://dx.doi.org/10.51593/20190042.
Texto completoRduner, Tim G. J. y Helen Toner. Key Concepts in AI Safety: Specification in Machine Learning. Center for Security and Emerging Technology, diciembre de 2021. http://dx.doi.org/10.51593/20210031.
Texto completoLu, Michael y Tina Gibson. Development of Predictive Tools for Anti-Cancer Peptide Candidates using Generative Machine Learning Models. Journal of Young Investigators, mayo de 2021. http://dx.doi.org/10.22186/jyi.39.5.60-64.
Texto completoMa, Zhegang, Han Bao, Sai Zhang, Min Xian y Andrea Mack. Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants. Office of Scientific and Technical Information (OSTI), febrero de 2022. http://dx.doi.org/10.2172/1847070.
Texto completoGates, Allison, Samantha Guitard, Jennifer Pillay, Sarah A. Elliott, Michele P. Dyson, Amanda S. Newton y Lisa Hartling. Performance and Usability of Machine Learning for Screening in Systematic Reviews: A Comparative Evaluation of Three Tools. Agency for Healthcare Research and Quality (AHRQ), noviembre de 2019. http://dx.doi.org/10.23970/ahrqepcmethmachineperformance.
Texto completoRudner, Tim y Helen Toner. Key Concepts in AI Safety: Robustness and Adversarial Examples. Center for Security and Emerging Technology, marzo de 2021. http://dx.doi.org/10.51593/20190041.
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