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Добірка наукової літератури з теми "Methods of machine learning"

Автор: Grafiati
Опубліковано: 28 травня 2022
Оновлено: 30 травня 2022

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Статті в журналах з теми "Methods of machine learning"

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Mubarakova,, S. R., S. T. Amanzholova,, and R. K. Uskenbayeva,. "USING MACHINE LEARNING METHODS IN CYBERSECURITY." Eurasian Journal of Mathematical and Computer Applications 10, no. 1 (2022): 69–78. http://dx.doi.org/10.32523/2306-6172-2022-10-1-69-78.

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Abstract Cybersecurity is an ever-changing field, with advances in technology that open up new opportunities for cyberattacks. In addition, even though serious secu- rity breaches are often reported, small organizations still have to worry about security breaches as they can often be the target of viruses and phishing. This is why it is so important to ensure the privacy of your user profile in cyberspace. The past few years have seen a rise in machine learning algorithms that address major cybersecu- rity issues such as intrusion detection systems (IDS), detection of new modifications of known malware, malware, and spam detection, and malware analysis. In this arti- cle, algorithms have been analyzed using data mining collected from various libraries, and analytics with additional emerging data-driven models to provide more effective security solutions. In addition, an analysis was carried out of companies that are en- gaged in cyber attacks using machine learning. According to the research results, it was revealed that the concept of cybersecurity data science allows you to make the computing process more efficient and intelligent compared to traditional processes in the field of cybersecurity. As a result, according to the results of the study, it was revealed that machine learning, namely unsupervised learning, is an effective method of dealing with risks in cybersecurity and cyberattacks.
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Turčaník, Michal. "Network User Behaviour Analysis by Machine Learning Methods." Information & Security: An International Journal 50 (2021): 66–78. http://dx.doi.org/10.11610/isij.5014.

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Bzdok, Danilo, Martin Krzywinski, and Naomi Altman. "Machine learning: supervised methods." Nature Methods 15, no. 1 (2018): 5–6. http://dx.doi.org/10.1038/nmeth.4551.

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BI, Hua, Hong-Li LIANG, and Jue WANG. "Resampling Methods and Machine Learning." Chinese Journal of Computers 32, no. 5 (2009): 862–77. http://dx.doi.org/10.3724/sp.j.1016.2009.00862.

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Hofmann, Thomas, Bernhard Schölkopf, and Alexander J. Smola. "Kernel methods in machine learning." Annals of Statistics 36, no. 3 (2008): 1171–220. http://dx.doi.org/10.1214/009053607000000677.

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Mitchell, John B. O. "Machine learning methods in chemoinformatics." Wiley Interdisciplinary Reviews: Computational Molecular Science 4, no. 5 (2014): 468–81. http://dx.doi.org/10.1002/wcms.1183.

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Shoup, T. E. "Machine learning—Paradigms and methods." Mechanism and Machine Theory 26, no. 3 (1991): 349. http://dx.doi.org/10.1016/0094-114x(91)90075-f.

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Facciorusso, Antonio, Raffaele Licinio, and Alfredo Di Leo. "Machine Learning Methods in Gastroenterology." Gastroenterology 149, no. 4 (2015): 1128–29. http://dx.doi.org/10.1053/j.gastro.2015.03.056.

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Rahangdale, Ashwini, and Shital Raut. "Machine Learning Methods for Ranking." International Journal of Software Engineering and Knowledge Engineering 29, no. 06 (2019): 729–61. http://dx.doi.org/10.1142/s021819401930001x.

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Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.
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Et. al., Zakoldaev D. A. ,. "Machine Learning Methods Performance Evaluation*." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2664–66. http://dx.doi.org/10.17762/turcomat.v12i2.2284.

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In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.
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Дисертації з теми "Methods of machine learning"

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Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.

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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of predictive accuracy. Specifically, ML models perform 49-100\% better than unbiased methods. However, we cannot rely on parameter estimations. For example, Chapter 4 introduced a net prediction problem regarding fraudulent property claims in insurance. Despite the fact that we got extraordinary results in terms of predictive power, the complexity of the problem restricted us from getting behavioral insight. Contrarily, statistical models are easily interpretable. Coefficients give us the sign, the magnitude and the statistical significance. We can learn behavior from marginal impacts and elasticities. Chapter 5 analyzed another prediction problem in the insurance market, particularly, how the combination of self-reported data and risk categorization could improve the detection of risky potential customers in insurance markets. Results were also quite impressive in terms of prediction, but again, we did not know anything about the direction or the magnitude of the features. However, by using a Probit model, we showed the benefits of combining statistic models with ML-DL models. The Probit model let us get generalizable insights on what type of customers are likely to misreport, enhancing our results. Likewise, Chapter 2 is a clear example of how causal inference can benefit from ML and DL methods. These techniques allowed us to capture that 70 days before each auction there were abnormal behaviors in daily prices. By doing so, we could apply a solid statistical model and we could estimate precisely what the net effect of the mandated auctions in Spain was. This thesis aims at combining advantages of both methodologies, machine learning and econometrics, boosting their strengths and attenuating their weaknesses. Thus, we used ML and statistical methods side by side, exploring predictive performance and interpretability. Several conditions can be inferred from the nature of both approaches. First, as we have observed throughout the chapters, ML and traditional econometric approaches solve fundamentally different problems. We use ML and DL techniques to predict, not in terms of traditional forecast, but making our models generalizable to unseen data. On the other hand, traditional econometrics has been focused on causal inference and parameter estimation. Therefore, ML is not replacing traditional techniques, but rather complementing them. Second, ML methods focus in out-of-sample data instead of in-sample data, while statistical models typically focus on goodness-of-fit. It is then not surprising that ML techniques consistently outperformed traditional techniques in terms of predictive accuracy. The cost is then biased estimators. Third, the tradition in economics has been to choose a unique model based on theoretical principles and to fit the full dataset on it and, in consequence, obtaining unbiased estimators and their respective confidence intervals. On the other hand, ML relies on data driven selection models, and does not consider causal inference. Instead of manually choosing the covariates, the functional form is determined by the data. This also translates to the main weakness of ML, which is the lack of inference of the underlying data-generating process. I.e. we cannot derive economically meaningful conclusions from the coefficients. Focusing on out-of-sample performance comes at the expense of the ability to infer causal effects, due to the lack of standard errors on the coefficients. Therefore, predictors are typically biased, and estimators may not be normally distributed. Thus, we can conclude that in terms of out-sample performance it is hard to compete against ML models. However, ML cannot contend with the powerful insights that the causal inference analysis gives us, which allow us not only to get the most important variables and their magnitude but also the ability to understand economic behaviors.
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VELLOSO, SUSANA ROSICH SOARES. "SQLLOMINING: FINDING LEARNING OBJECTS USING MACHINE LEARNING METHODS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10970@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Objetos de Aprendizagem ou Learning Objects (LOs) são porções de material didático tais como textos que podem ser reutilizados na composição de outros objetos maiores (aulas ou cursos). Um dos problemas da reutilização de LOs é descobri-los em seus contextos ou documentos texto originais tais como livros, e artigos. Visando a obtenção de LOs, este trabalho apresenta um processo que parte da extração, tratamento e carga de uma base de dados textual e em seguida, baseando-se em técnicas de aprendizado de máquina, uma combinação de EM (Expectation-Maximization) e um classificador Bayesiano, classifica-se os textos extraídos. Tal processo foi implementado em um sistema chamado SQLLOMining, que usa SQL como linguagem de programação e técnicas de mineração de texto na busca de LOs.<br>Learning Objects (LOs) are pieces of instructional material like traditional texts that can be reused in the composition of more complex objects like classes or courses. There are some difficulties in the process of LO reutilization. One of them is to find pieces of documents that can be used like LOs. In this work we present a process that, in search for LOs, starts by extracting, transforming and loading a text database and then continue clustering these texts, using a machine learning methods that combines EM (Expectation- Maximization) and a Bayesian classifier. We implemented that process in a system called SQLLOMining that uses the SQL language and text mining methods in the search for LOs.
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Berry, Jeffrey James. "Machine Learning Methods for Articulatory Data." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223348.

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Humans make use of more than just the audio signal to perceive speech. Behavioral and neurological research has shown that a person's knowledge of how speech is produced influences what is perceived. With methods for collecting articulatory data becoming more ubiquitous, methods for extracting useful information are needed to make this data useful to speech scientists, and for speech technology applications. This dissertation presents feature extraction methods for ultrasound images of the tongue and for data collected with an Electro-Magnetic Articulograph (EMA). The usefulness of these features is tested in several phoneme classification tasks. Feature extraction methods for ultrasound tongue images presented here consist of automatically tracing the tongue surface contour using a modified Deep Belief Network (DBN) (Hinton et al. 2006), and methods inspired by research in face recognition which use the entire image. The tongue tracing method consists of training a DBN as an autoencoder on concatenated images and traces, and then retraining the first two layers to accept only the image at runtime. This 'translational' DBN (tDBN) method is shown to produce traces comparable to those made by human experts. An iterative bootstrapping procedure is presented for using the tDBN to assist a human expert in labeling a new data set. Tongue contour traces are compared with the Eigentongues method of (Hueber et al. 2007), and a Gabor Jet representation in a 6-class phoneme classification task using Support Vector Classifiers (SVC), with Gabor Jets performing the best. These SVC methods are compared to a tDBN classifier, which extracts features from raw images and classifies them with accuracy only slightly lower than the Gabor Jet SVC method.For EMA data, supervised binary SVC feature detectors are trained for each feature in three versions of Distinctive Feature Theory (DFT): Preliminaries (Jakobson et al. 1954), The Sound Pattern of English (Chomsky and Halle 1968), and Unified Feature Theory (Clements and Hume 1995). Each of these feature sets, together with a fourth unsupervised feature set learned using Independent Components Analysis (ICA), are compared on their usefulness in a 46-class phoneme recognition task. Phoneme recognition is performed using a linear-chain Conditional Random Field (CRF) (Lafferty et al. 2001), which takes advantage of the temporal nature of speech, by looking at observations adjacent in time. Results of the phoneme recognition task show that Unified Feature Theory performs slightly better than the other versions of DFT. Surprisingly, ICA actually performs worse than running the CRF on raw EMA data.
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Khan, Muhammad Naeem Ahmed. "Digital Forensics using Machine Learning Methods." Thesis, University of Sussex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487975.

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The increase in computer related crimes, with particular reference to internet crimes, has led to an increasing demand for state-of-the-art digital forensics. Reconstruction of the past events in chronological order is crucial for digital forensic investigations to pinpoint the execution of relevant application programs and the files manipulated by those applications. The event reconstruction process can be made more objective and rigorous by employing mathematical techniques due to their sound theoretical foundations. The focus of this research is to explore the effectiveness of employing machine learning methodologies for computer forensic analysis by tracing past file system activities and preparing a timeline to facilitate the identification of incriminating evidence. A general criterion for measuring the efficacy of an analysis tool is to corroborate how well the analysis responds to the unforeseen evidence. The generation of a comprehensive timeline of the past events becomes more complicated if some information is missing or certain sources of evidence are contaminated or scrubbed. This thesis provides a genuine contribution to digital forensics research by focusing on the identification of the execution of application programs - a vital area which is not usually directly accessible from the available data. In addition to the neural network techniques; a Bayesian approach for data classification has been explored, this addresses the issue of missing/incomplete data. Bayesian methodology is an improvement over the existing ad hoc digital forensic analysis approaches carried out in bits and pieces. The Bayesian and neural networks techniques have produced encouraging results and these results are reported herein.
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Li, Limin, and 李丽敏. "Machine learning methods for computational biology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44546749.

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Marakani, Sumeesha. "Employee Matching Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18493.

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Background: Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individuals. Objectives: This master thesis is a collaboration with Volvo Cars to attempt applying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free-flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques. Methods: Through the course of this project, various techniques are employed and experimented with to study, analyze and understand the unlabelled textual data using NLP techniques. Through the course of the project, we try to match individuals based on information extracted from these techniques using Unsupervised MachineLearning methods (K-means clustering).Results. The results obtained from applying the various NLP techniques are explained along with the algorithms that are implemented. Inferences deduced about the properties of the data and methodologies are discussed. Conclusions: The results obtained from this project have shown that it is possible to extract patterns among people based on free-text data written about them. The future aim is to incorporate the semantic relationship between the words to be able to identify people who are similar and dissimilar based on the data they share about themselves.
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Chlon, Leon. "Machine learning methods for cancer immunology." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/268068.

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Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving immunotherapy initiatives. However, progress in this domain is hindered by an incomplete characterisation of the regulatory mechanisms involved in cancer immunity. Addressing this challenge, this thesis is formulated around a fundamental line of inquiry: how do we quantitatively describe the immune system with respect to tumour heterogeneity? Describing the molecular interactions between cancer cells and the immune system is a fundamental goal of cancer immunology. The first part of this thesis describes a three-stage association study to address this challenge in pancreatic ductal adenocarcinoma (PDAC). Firstly, network-based approaches are used to characterise PDAC on the basis of transcription factor regulators of an oncogenic KRAS signature. Next, gene expression tools are used to resolve the leukocyte subset mixing proportions, stromal contamination, immune checkpoint expression and immune pathway dysregulation from the data. Finally, partial correlations are used to characterise immune features in terms of KRAS master regulator activity. The results are compared across two independent cohorts for consistency. Moving beyond associations, the second part of the dissertation introduces a causal modelling approach to infer directed interactions between signaling pathway activity and immune agency. This is achieved by anchoring the analysis on somatic genomic changes. In particular, copy number profiles, transcriptomic data, image data and a protein-protein interaction network are integrated using graphical modelling approaches to infer directed relationships. Generated models are compared between independent cohorts and orthogonal datasets to evaluate consistency. Finally, proposed mechanisms are cross-referenced against literature examples to test for legitimacy. In summary, this dissertation provides methodological contributions, at the levels of associative and causal inference, for inferring the contextual basis for tumour-specific immune agency.
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Chang, Allison An. "Integer optimization methods for machine learning." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72643.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 129-137).<br>In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in diverse domains such as information retrieval, recommender systems, natural language processing, bioinformatics, and preventative maintenance. The primary advantage of using MIO for ranking is that it allows for direct optimization of ranking quality measures, as opposed to current state-of-the-art algorithms that use heuristic loss functions. We demonstrate using a number of datasets that our approach can outperform other ranking methods. The second part of the thesis focuses on reverse-engineering ranking models. This is an application of a more general ranking problem than the bipartite case. Quality rankings affect business for many organizations, and knowing the ranking models would allow these organizations to better understand the standards by which their products are judged and help them to create higher quality products. We introduce an MIO method for reverse-engineering such models and demonstrate its performance in a case-study with real data from a major ratings company. We also devise an approach to find the most cost-effective way to increase the rank of a certain product. In the final part of the thesis, we develop MIO methods to first generate association rules and then use the rules to build an interpretable classifier in the form of a decision list, which is an ordered list of rules. These are both combinatorially challenging problems because even a small dataset may yield a large number of rules and a small set of rules may correspond to many different orderings. We show how to use MIO to mine useful rules, as well as to construct a classifier from them. We present results in terms of both classification accuracy and interpretability for a variety of datasets.<br>by Allison An Chang.<br>Ph.D.
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Lowe, Robert Alexander. "Investigating machine learning methods in chemistry." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610567.

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Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.<br>Detta examensarbete, utfört på Ericsson AB, ämnar att undersöka huruvida maskininlärningstekniker kan användas för att klassificera dumpfiler för mer effektiv problemidentifiering. Projektet fokuserar på övervakad inlärning och då speciellt Bayesiansk klassificering. Arbetet visar att ett program som utnyttjar Bayesiansk klassificering kan uppnå en noggrannhet väl över slumpen. Arbetet indikerar att maskininlärningstekniker mycket väl kan komma att bli användbara alternativ till mänsklig klassificering av dumpfiler i en nära framtid.
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Книги з теми "Methods of machine learning"

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Savoy, Jacques. Machine Learning Methods for Stylometry. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53360-1.

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Fielding, Alan H. Machine Learning Methods for Ecological Applications. Springer US, 1999.

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Zhang, Cha. Ensemble Machine Learning: Methods and Applications. Springer US, 2012.

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Hutter, Frank. Automated Machine Learning: Methods, Systems, Challenges. Springer Nature, 2019.

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Fielding, Alan H., ed. Machine Learning Methods for Ecological Applications. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5.

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Winkler, Joab, Mahesan Niranjan, and Neil Lawrence, eds. Deterministic and Statistical Methods in Machine Learning. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559887.

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Eguchi, Shinto, and Osamu Komori. Minimum Divergence Methods in Statistical Machine Learning. Springer Japan, 2022. http://dx.doi.org/10.1007/978-4-431-56922-0.

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Berrar, Daniel. Machine learning methods for analyzing DNA microarray data. The Author), 2004.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and José Crossa. Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0.

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Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Information Science Reference, 2010.

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Частини книг з теми "Methods of machine learning"

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Chu, Liu. "Machine Learning Methods." In Uncertainty Quantification of Stochastic Defects in Materials. CRC Press, 2021. http://dx.doi.org/10.1201/9781003226628-7.

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Ghosh, Shyamasree, and Rathi Dasgupta. "Machine Learning Methods." In Machine Learning in Biological Sciences. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8881-2_3.

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Dawson, Catherine. "Machine learning." In A–Z of Digital Research Methods. Routledge, 2019. http://dx.doi.org/10.4324/9781351044677-30.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Machine Learning." In Data Mining Methods for Knowledge Discovery. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.

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Lampropoulos, Aristomenis S., and George A. Tsihrintzis. "Cascade Recommendation Methods." In Machine Learning Paradigms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19135-5_6.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Newton’s Methods." In Machine Learning in Medicine. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7869-6_16.

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Mannor, Shie, Xin Jin, Jiawei Han, et al. "Kernel Methods." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_430.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, et al. "Bayesian Methods." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_63.

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Golden, Richard M. "Simulation Methods for Evaluating Generalization." In Statistical Machine Learning. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507-14.

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Lampropoulos, Aristomenis S., and George A. Tsihrintzis. "Evaluation of Cascade Recommendation Methods." In Machine Learning Paradigms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19135-5_7.

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Тези доповідей конференцій з теми "Methods of machine learning"

1

Singh, Amitojdeep, Sourya Sengupta, and Vasudevan Lakshminarayanan. "Glaucoma diagnosis using transfer learning methods." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2529429.

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Maniar, Hiren, Srikanth Ryali, Mandar S. Kulkarni, and Aria Abubakar. "Machine-learning methods in geoscience." In SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, 2018. http://dx.doi.org/10.1190/segam2018-2997218.1.

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3

Klauninger, Bert, Martin Unger, and Horst Eidenberger. "Machine Learning with Dual Process Models." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005655901480153.

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4

Volzhina, Elena. "MACHINE LEARNING METHODS IN WEATHER FORECASTS." In 19th SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings. STEF92 Technology, 2019. http://dx.doi.org/10.5593/sgem2019/2.1/s07.051.

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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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Poymanova, E. D., and T. M. Tatarnikova. "Applying Machine Learning Methods for Forecasting." In 2020 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). IEEE, 2020. http://dx.doi.org/10.1109/weconf48837.2020.9131480.

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Yeu, Chee-Wee Thomas, Meng-Hiot Lim, and Guang-Bin Huang. "Terrain Modeling Using Machine Learning Methods." In 2006 9th International Conference on Control, Automation, Robotics and Vision. IEEE, 2006. http://dx.doi.org/10.1109/icarcv.2006.345471.

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Geyik, Buket, and Medine Kara. "Severity Prediction with Machine Learning Methods." In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2020. http://dx.doi.org/10.1109/hora49412.2020.9152601.

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Todorova, Maya, and Ginka Marinova. "Methods of Machine Learning in Oncology." In 2020 XXIX International Scientific Conference Electronics (ET). IEEE, 2020. http://dx.doi.org/10.1109/et50336.2020.9238263.

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Krichevsky, M. L. "Personnel Audit By Machine Learning Methods." In II International Conference on Economic and Social Trends for Sustainability of Modern Society. European Publisher, 2021. http://dx.doi.org/10.15405/epsbs.2021.09.02.306.

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Звіти організацій з теми "Methods of machine learning"

1

Vesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493534.

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2

Xu, Yuesheng. Adaptive Kernel Based Machine Learning Methods. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada588768.

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Zhang, Tong. Multi-Stage Convex Relaxation Methods for Machine Learning. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada580533.

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Jesneck, Jonathan, and Joseph Lo. Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430017.

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Semen, Peter M. A Generalized Approach to Soil Strength Prediction With Machine Learning Methods. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada464726.

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Chernozhukov, Victor, Kaspar Wüthrich, and Yinchu Zhu. Exact and robust conformal inference methods for predictive machine learning with dependent data. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.1618.

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Hemphill, Geralyn M. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1329544.

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NOBRE, GUSTAVO, G. Nobre, D. Brown, et al. Expansion of Machine-Learning Method for Classifying Neutron Resonances. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1823638.

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Valaitis, Vytautas, and Alessandro T. Villa. A Machine Learning Projection Method for Macro-Finance Models. Federal Reserve Bank of Chicago, 2022. http://dx.doi.org/10.21033/wp-2022-19.

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